Adaptive recommendations systems

ABSTRACT

An adaptive recommendation system and a mobile adaptive recommendation system are disclosed. The adaptive recommendation system and the mobile adaptive recommendation system include algorithms for monitoring user usage behaviors across a plurality of usage behavior categories associated with a computer-based system, and generating recommendations based on inferences on user preferences and interests. Privacy control functions and compensatory functions related to insincere usage behaviors can be applied. Adaptive recommendation delivery can take the form of visual-based or audio-based formats.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority under 35 U.S.C. § 119 to PCTInternational Application No. PCT/US2004/037176, which claimed priorityunder 35 U.S.C. § 119(e) to U.S. Provisional Patent Application Ser. No.60/525,120, entitled “A Method and System for Adaptive Fuzzy Networks,”filed Nov. 28, 2003. The present application is a reissue application ofU.S. patent application Ser. No. 11/419,547, filed May 22, 2006, nowU.S. Pat. No. 7,526,458, issued Apr. 28, 2009, entitled ADAPTIVERECOMMENDATIONS SYSTEM; which is a continuation of PCT InternationalApplication No. PCT/US2004/037176, entitled ADAPTIVE RECOMBINANTSYSTEMS, filed Nov. 4, 2004, now expired; which claims the benefit ofU.S. Provisional Patent Application Ser. No. 60/525,120, entitled AMETHOD AND SYSTEM FOR ADAPTIVE FUZZY NETWORKS filed Nov. 28, 2003.

FIELD OF THE INVENTION

This invention relates to software programs that adapt according totheir use over time, and that may be distributed and recombined as awhole or in part across one or more computer systems.

BACKGROUND OF THE INVENTION

Current general purpose computer-based information management approachesinclude flat files, hypertext models (e.g., World Wide Web), andrelational database management systems (RDBMS). A fundamental problemwith all of these approaches is “brittleness”—they have limited inherentability to adapt to changing circumstances without direct humanintervention. For the more robust of these information managementapproaches (e.g., relational database management system, or RDBMS), thehuman intervention may be somewhat reduced compared to that of lesssophisticated approaches (e.g., flat files), but the need for direct,manual effort is certainly not eliminated.

Likewise, specific computer applications that are underpinned by theprior art information management approaches are generally very limitedin their ability to adapt to changing circumstances and userrequirements over time. In addition to prior art information managementapproaches and the computing applications built on them generally beingtoo brittle, they also can be criticized for being monolithic—that is,it is generally not possible to dynamically separate subsets of acomputing application and recombine them with other subsets of aplurality of computing applications to form new and useful applications.In other words, prior art computing systems and applications are verylimited in their ability to usefully evolve without directed humanprogramming or content management attention. This is a significant rootcause of the well-known and well-discussed “software bottleneck.”

SUMMARY OF INVENTION

An adaptive recombinant system is disclosed to address the problems oflimited adaptation and extensibility associated with prior art computingapplications by incorporating an information management and computingsystem paradigm that has built-in capabilities to facilitate adaptationto changing circumstances and user requirements and preferences. Theadaptive recombinant system can track, store and make user preferenceand interest inferences from a broad array of system usage behaviors.These inferencing capabilities may be applied to not only assist systemusers in more effectively navigating the system, but may also be appliedto modify system structure and content so as to embed adaptationdirectly within the system and content, thereby enabling the system toevolve to become ever more effective over time.

Furthermore, users of the system may themselves be represented orexplicitly referenced within system content. Fundamentally, the adaptiverecombinant system represents a computer-based systems architecture inwhich system users may be represented directly within the system contentand structure, and the usage behaviors over time of the users may beembedded directly in the system structure. Thus, the adaptiverecombinant system explicitly integrates the system, users of thesystem, and usage of the system in a way that extends beyond the lessintegrative, and more ad hoc approaches of prior art; thereby enabling ahigher degree of computer-based system adaptiveness and extensibility.The adaptive recombinant system can complement current informationmanagement and computer application approaches to enable the resultingoverall system to be more adaptive to individual and community userrequirements.

In some embodiments, a network (where the term “network” is used as aterm denoting a general system topology, not to be confused withspecific application or use of the term, such as, for example, a“telecommunications network”) system structure is employed to facilitateadequate structural plasticity to enable system adaptation, and toenable syndication and combinations of system subsets. The network-basedsystem structure may furthermore be based on a fuzzy network or fuzzycontent network architecture.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a black diagram of an adaptive system, according to someembodiments;

FIG. 2 is a block diagram contrasting the adaptive system of FIG. 1 witha non-adaptive system, according to some embodiments;

FIG. 3A is a block diagram of the structural aspect of the adaptivesystem of FIG. 1, according to some embodiments;

FIG. 3B is a block diagram of the content aspect of the adaptive systemof FIG. 1, according to some embodiments;

FIG. 3C is a block diagram of the usage aspect of the adaptive system ofFIG. 1, according to some embodiments;

FIG. 4 is a block diagram showing structural subsets generated by theadaptive recommendations function of FIG. 1, according to someembodiments;

FIG. 5 is a block diagram of the adaptive recommendations function usedby the adaptive system of FIG. 1, according to some embodiments;

FIG. 6 is a block diagram describing a generalized adaptive systemfeedback flow, according to some embodiments;

FIG. 7 is a block diagram of a public information framework used by theadaptive system of FIG. 1, according to some embodiments;

FIG. 8 is a diagram of user communities, according to some embodiments;

FIG. 9 is a diagram of user communities and associated relationships,according to some embodiments;

FIG. 10 is a flow chart showing how recommendations of the adaptivesystem 100 of FIG. 1 are generated, whether to support system navigationand use or to update structural or content aspects of the adaptivesystem, according to some embodiments;

FIG. 11 is a block diagram depicting the different user types supportedby the adaptive system of FIG. 1, according to some embodiments;

FIG. 12 is a block diagram depicting how users of the adaptive system ofFIG. 1 may be associated with the content aspect, according to someembodiments;

FIGS. 13A and 13B are block diagrams of non-fuzzy, non-directed networksystem structures with single or multiple relationship types, accordingto the prior art;

FIG. 14A is a block diagram illustrating alternative representations ofa non-fuzzy, non-directed network system structure, according to theprior art;

FIG. 14B is a block diagram illustrating alternative representations ofa fuzzy, directed network system structure, according to the prior art;

FIGS. 15A and 15B are block diagrams of non-fuzzy, directed networksystem structures with single or multiple relationship types accordingto the prior art;

FIGS. 16A and 16B are block diagrams of fuzzy, non-directed networksystem structures with single or multiple relationship types accordingto the prior art;

FIGS. 17A and 17B are block diagrams of fuzzy, directed network systemstructures with single or multiple relationship types according to theprior art;

FIG. 18 is a block diagram of an adaptive recombinant system, accordingto some embodiments;

FIG. 19 is a block diagram of the syndication function used by theadaptive recombinant system of FIG. 18, according to some embodiments;

FIG. 20 is a block diagram of the fuzzy network operators used by theadaptive recombinant system of FIG. 18, according to some embodiments;

FIG. 21 is a block diagram illustrating degrees of separation betweennodes in a non-fuzzy network, according to the prior art;

FIG. 22 is a block diagram illustrating fractional degree of separationof nodes in a fuzzy network, according to some embodiments;

FIG. 23 is a block diagram illustrating a network subset based onfractional degree of separation selection criteria in the non-fuzzynetwork of FIG. 21, according to the prior art;

FIG. 24 is a block diagram illustrating a network subset based onfractional degree of separation selection criteria in the fuzzy networkof FIG. 22, according to some embodiments;

FIG. 25 is a block diagram illustrating a fuzzy network metric ofinfluence for designated neighborhoods based on fractional degrees ofseparation according to some embodiments;

FIG. 26 is a block diagram of a fuzzy network selection operationaccording to some embodiments;

FIG. 27 is a block diagram of the adaptive system of FIG. 1 in which thestructural aspect is a fuzzy network, according to some embodiments;

FIG. 28 is a block diagram of the adaptive recombinant system of FIG. 18in which the structural aspect is a fuzzy network, according to someembodiments;

FIG. 29 is a block diagram of a structural aspect including multiplenetwork-based structures, according to some embodiments;

FIG. 30 is a block diagram of a fuzzy network union operation, accordingto some embodiments;

FIGS. 31A-31D are block diagrams illustrating syndication of fuzzynetworks and fuzzy network subsets, according to some embodiments;

FIG. 32 is a block diagram of the adaptive recombinant system of FIG.18, in which multiple adaptive systems are simultaneously supported,according to some embodiments;

FIG. 33 is a block diagram of a fuzzy content network, according to someembodiments;

FIGS. 34A-34C are block diagrams of an object, a topic object, and acontent object for the fuzzy content network of FIG. 33, according tosome embodiments;

FIG. 35 is a block diagram of the adaptive system of FIG. 1 in which thestructural aspect is a fuzzy content network, according to someembodiments;

FIG. 36 is a block diagram of the adaptive recombinant system of FIG. 18in which the structural aspect is a fuzzy content network, according tosome embodiments;

FIG. 37 is a block diagram of a fuzzy content network object structurebased on an extended fractional degrees of separation architecture,according to some embodiments;

FIG. 38 is a screen image of the Epiture “My World” function, accordingto some embodiments;

FIG. 39 is a screen image of the Epiture “Trends” function, according tosome embodiments;

FIG. 40 is a screen image of the Epiture “MyPaths” function, accordingto some embodiments;

FIG. 41 is a screen image of the Epiture adaptive recommendationsfunction, according to some embodiments;

FIG. 42 is a diagram of a framework for categorizing adaptive systems,according to some embodiments;

FIG. 43 is a flow diagram of the adaptive recommendations function ofthe Epiture software system, according to some embodiments;

FIGS. 44A and 44B are block diagrams illustrating fuzzy networkstructural modifications through application of adaptive recommendationfunctions, according to some embodiments; and

FIG. 45 is a diagram of various computing device topologies, accordingto some embodiments.

DETAILED DESCRIPTION

In accordance with the embodiments described herein, an adaptive system,an adaptive recombinant system, and methods for establishing the systemsare disclosed. The adaptive system includes algorithms for tracking userinteractions with a collection of system objects, and generates adaptiverecommendations based on the usage behaviors associated with the systemobjects. The adaptive recommendations may be explicitly represented tothe user or may be used to automatically update the collection of systemobjects and associated relationships. In either case, the collection ofobjects and associated relationships become more useful to the user overtime.

The adaptive recombinant system, which includes the adaptive system, mayfurther be syndicated to other computer applications, including otheradaptive systems. The adaptive recombinant system may recombine andresyndicate indefinitely. Both the adaptive system and the adaptiverecombinant system may be based on a fuzzy network or a fuzzy contentnetwork structure.

The adaptive system may be implemented on a single computer or onmultiple computers that are connected through a network, such as theInternet. The software and data storage associated with the adaptivesystem may reside on the single computer, or may be distributed acrossthe multiple computers. The adaptive system may be implemented onstationary computers, on mobile computing devices, on processing unitsarchitected according to Von Neumann designs, or on those designedaccording to non-Von Neumann architectures. The adaptive system mayintegrate with existing types of computer software, such as computeroperating systems, including mobile device operating systems and specialpurpose devices, such as television “set-top boxes,” network operatingsystems, database software, application middleware, and applicationsoftware, such as enterprise resource planning (ERP) applications,desktop productivity tools, Internet applications, and so on.

In the following description, numerous details are set forth to providean understanding of the present invention. However, it will beunderstood by those skilled in the art that the present invention may bepracticed without these details and that numerous variations ormodifications from the described embodiments may be possible.

Adaptive System

FIG. 1 is a generalized depiction of an adaptive system 100, accordingto some embodiments. The adaptive system 100 includes three aspects: 1)a structural aspect 210, a usage aspect 220, and a content aspect 230.One or more users 200 interact with the adaptive system 100. An adaptiverecommendations function 240 may produce adaptive recommendations 250,based upon the user interactions that are either delivered to the user200 or applied to the adaptive system 100.

As used herein, one or more users 200 may be a single user or multipleusers. As shown in FIG. 1, the one or more users 200 may receive theadaptive recommendations 250. Non-users 260 of the adaptive system 100may also receive adaptive recommendations 250 from the adaptive system100.

A user 200 may be a human entity, a computer system, or a secondadaptive system (distinct from the adaptive system 100) that interactswith, or otherwise uses the adaptive system. The one or more users 200may include non-human users of the adaptive system 100. In particular,one or more other adaptive systems may serve as virtual system “users.”These other adaptive systems may operate in accordance with thearchitecture of the adaptive system 100. Thus, multiple adaptive systemsmay be mutual users for one another.

FIG. 2 distinguishes between the adaptive system 100 of FIG. 1 and anon-adaptive system, as used herein. A non-adaptive system 258 is acomputer-based system including at least the structural aspect 210 andthe content aspect 230, but without the usage aspect 220 and adaptiverecommendations function 240. (These terms are defined with morespecificity below.) The adaptive system 100 is a computer-based systemincluding at least a structural aspect 210, a content aspect 230, ausage aspect 220, and an adaptive recommendations function 240.

It should be understood that the structural aspect 210, the contentaspect 230, the usage aspect 220, and the recommendations function 240of the adaptive system 100, and elements of each, may be containedwithin one computer, or distributed among multiple computers.Furthermore, one or more non-adaptive systems 258 may be modified tobecome one or more adaptive systems 100 by integrating the usage aspect220 and the recommendations function 240 with the one or morenon-adaptive systems 258.

The term “computer system” or the term “system,” without furtherqualification, as used herein, will be understood to mean either anon-adaptive or an adaptive system. Likewise, the terms “systemstructure” or “system content,” as used herein, will be understood torefer to the structural aspect 210 and the content aspect 230,respectively, whether associated with the non-adaptive system 258 or theadaptive system 100. The term “system structural subset” or “structuralsubset,” as used herein, will be understood to mean a portion or subsetof the structural aspect 210 of a system.

Structural Aspect

The structural aspect 210 of the adaptive system 100 is depicted in theblock diagram of FIG. 3A. The structural aspect 210 denotes a collectionof system objects 212 that are part of the adaptive system 100, as wellas the relationships among the objects 214. The relationships amongobjects 214 may be persistent across user sessions, or may be transientin nature. The objects 212 may include or reference items of content,such as text, graphics, audio, video, interactive content, or embody anyother type or item of information. The objects 212 may also includereferences to content, such as pointers. Computer applications,executable code, or references to computer applications may also bestored as objects 212 in the adaptive system 100. The content of theobjects 212 is known herein as information 232. The information 232,though part of the object 214, is also considered part of the contentaspect 230, as depicted in FIG. 3B, and described below.

The objects 212 may be managed in a relational database, or may bemaintained in structures such as flat files, linked lists, invertedlists, hypertext networks, or object-oriented databases. The objects 212may include meta-information 234 associated with the information 232contained within, or referenced by the objects 212.

As an example, in some embodiments, the World-wide Web could beconsidered a structural aspect, where web pages constitute the objectsof the structural aspect and links between web pages constitute therelationships among the objects. Alternatively, or in addition, in someembodiments, the structural aspect could be comprised of objectsassociated with an object-oriented programming language, and therelationships between the objects associated with the protocols andmethods associated with interaction and communication among the objectsin accordance with the object-oriented programming language.

The one or more users 200 of the adaptive system 100 may be explicitlyrepresented as objects 212 within the system 100, therefore becomingdirectly incorporated within the structural aspect 210. Therelationships among objects 214 may be arranged in a hierarchicalstructure, a relational structure (e.g. according to a relationaldatabase structure), or according to a network structure.

Content Aspect

The content aspect 230 of the adaptive system 100 is depicted in theblock diagram of FIG. 3B. The content aspect 230 denotes the information232 contained in, or referenced by the objects 212 that are part of thestructural aspect 210. The content aspect 230 of the objects 212 mayinclude text, graphics, audio, video, and interactive forms of content,such as applets, tutorials, courses, demonstrations, modules, orsections of executable code or computer programs. The one or more users200 interact with the content aspect 230.

The content aspect 230 may be updated based on the usage aspect 220, aswell as associated metrics. To achieve this, the adaptive system 100 mayemploy the usage aspect of other systems. Such systems may include, butare not limited to, other computer systems, other networks, such as theWorld Wide Web, multiple computers within an organization, otheradaptive systems, or other adaptive recombinant systems. In this manner,the content aspect 230 benefits from usage occurring in otherenvironments.

Usage Aspect

The usage aspect 220 of the adaptive system 100 is depicted in the blockdiagram of FIG. 3C. The usage aspect 220 denotes captured usageinformation 202, further identified as usage behaviors 270, and usagebehavior pre-processing 204. The usage aspect 220 thus reflects thetracking, storing, categorization, and clustering of the use andassociated usage behaviors of the one or more users 200 interacting withthe adaptive system 100.

The captured usage information 202, known also as system usage or systemuse 202, includes any interaction by the one or more users 200 with thesystem. The adaptive system 100 tracks and stores user key strokes andmouse clicks, for example, as well as the time period in which theseinteractions occurred (e.g., timestamps), as captured usage information202. From this captured usage information 202, the adaptive system 100identifies usage behaviors 270 of the one or more users 200 (e.g., webpage access or email transmission). Finally, the usage aspect 220includes usage-behavior pre-processing, in which usage behaviorcategories 246, usage behavior clusters 247, and usage behavioralpatterns 248 are formulated for subsequent processing of the usagebehaviors 270 by the adaptive system 100. Some usage behaviors 270identified by the adaptive system 100, as well as usage behaviorcategories 246 designated by the adaptive system 100, are listed inTable 1, and described in more detail, below.

The usage behavior categories 246, usage behaviors clusters 247, andusage behavior patterns 248 may be interpreted with respect to a singleuser 200, or to multiple users 200, in which the multiple users may bedescribed herein as a community, an affinity group, or a user segment.These terms are used interchangeably herein. A community is a collectionof one or more users, and may include what is commonly referred to as a“community of interest.” A sub-community is also a collection of one ormore users, in which members of the sub-community include a portion ofthe users in a previously defined community. Communities, affinitygroups, and user segments are described in more detail, below.

Usage behavior categories 246 include types of usage behaviors 270, suchas accesses, referrals to other users, collaboration with other users,and so on. These categories and more are included in Table 1, below.Usage behavior clusters 247 are groupings of one or more usage behaviors270, either within a particular usage behavior category 246 or acrosstwo or more usage categories. The usage behavior pre-processing 204 mayalso determine new “clusterings” of user behaviors 270 in previouslyundefined usage behavior categories 246, across categories, or among newcommunities. Usage behavior patterns 248, also known as “usagebehavioral patterns” or “behavioral patterns,” are also groupings ofusage behaviors 270 across usage behavior categories 246. Usage behaviorpatterns 248 are generated from one or more filtered clusters ofcaptured usage information 202.

The usage behavior patterns 248 may also capture and organize capturedusage information 202 to retain temporal information associated withusage behaviors 270. Such temporal information may include the durationor timing of the usage behaviors 270, such as those associated withreading or writing of written or graphical material, oralcommunications, including listening and talking, or physical location ofthe user 200. The usage behavioral patterns 248 may includesegmentations and categorizations of usage behaviors 270 correspondingto a single user of the one or more users 200 or according to multipleusers 200 (e.g., communities or affinity groups). The communities oraffinity groups may be previously established, or may be generatedduring usage behavior pre-processing 204 based on inferred usagebehavior affinities or clustering. Usage behaviors 270 may also bederived from the use or explicit preferences 252 associated with otheradaptive or non-adaptive systems.

Adaptive Recommendations Function

Returning to FIG. 1, the adaptive system 100 includes an adaptiverecommendations function 240, which interacts with the structural aspect210, the usage aspect 220, and the content aspect 230. The adaptiverecommendations function 240 generates adaptive recommendations 250based on the integration and application of the structural aspect 210,the usage aspect 220, and, optionally, the content aspect 230.

The term “recommendations” associated with the adaptive recommendationsfunction 240 is used broadly in the adaptive system 100. The adaptiverecommendations 250 may be displayed to a recommendations recipient. Asused herein, a recommendations recipient is an entity who receives theadaptive recommendations 250. Thus, the recommendations recipient mayinclude the one or more users 200 of the adaptive system 100, asindicated by the dotted arrow 255 in FIG. 1, or a non-user 260 of thesystem (see dotted arrow 265). However, the adaptive recommendations 250may also be used internally by the adaptive system 100 to update thestructural aspect 210 (see dotted arrow 245). In this manner, the usagebehavior 270 of the one or more users 200 may be influenced by thesystem structural alterations that are automatically orsemi-automatically applied. Or, the adaptive recommendations 250 may beused by the adaptive system 100 to update the content aspect 230 (seedotted arrow 246).

FIG. 5 is a block diagram of the adaptive recommendations function 240used by the adaptive system 100 of FIG. 1. The adaptive recommendationsfunction 240 includes two algorithms, a preference inferencing algorithm242 and a recommendations optimization algorithm 244. These algorithms(which actually many include many more than two algorithms) are used bythe adaptive system 100 to generate adaptive recommendations 250.

Preferably, the adaptive system 100 identifies the preferences of theuser 200 and adapts the adaptive system 100 in view of the preferences.Preferences describe the likes, tastes, partiality, and/or predilectionof the user 200 that may be inferred during access of the objects 212 ofthe adaptive system 100. In general, user preferences exist consciouslyor sub-consciously within the mind of the user. Since the adaptivesystem 100 has no direct access to these preferences, they are generallyinferred by the preference inferencing algorithm 242 of the adaptiverecommendations function 240.

The preference inferencing algorithm 242, infers preferences based oninformation that may be obtained as the user 200 accesses the adaptivesystem 100. The preference inferencing algorithm and associated output242 is also described herein generally as “preference inferencing” or“preference inferences” of the adaptive system 100. The preferenceinferencing algorithm 242 identifies three types of preferences:explicit preferences 252, inferred preferences 253, and inferredinterests 254. Unless otherwise stated, the use of the term“preferences” herein is meant to include any or all of the elements 252,253, and 254 depicted in FIG. 5.

As used herein, explicit preferences 252 describe explicit choices ordesignations made by the user 200 during use of the adaptive system 100.The explicit preferences 252 may be considered to more explicitly revealpreferences than inferences associated with other types of usagebehaviors. A response to a survey is one example where explicitpreferences 252 may be identified by the adaptive system 100.

Inferred preferences 253 describe preferences of the user 200 that arebased on usage behavioral patterns 248. Inferred preferences 253 arederived from signals and cues made by the user 200. (The derivation ofinferred preferences 253 by the adaptive system 100 is included in thedescription of FIG. 7, below.)

Inferred interests 254 describe interests of the user 200 that are basedon usage behavioral patterns 248. In general, the adaptiverecommendations 250 produced by the preference inferencing algorithm 242combine inferences from overall user community behaviors andpreferences, inferences from sub-community or expert behaviors andpreferences, and inferences from personal user behaviors andpreferences. As used herein, preferences (whether explicit 252 orinferred 253) are distinguishable from interests (254) in thatpreferences imply a ranking (e.g., object A is better than object B)while interests do not necessarily imply a ranking.

A second algorithm 244, designated recommendations optimization 244,optimizes the adaptive recommendations 250 produced by the adaptivesystem 100. The adaptive recommendations 250 may be augmented byautomated inferences and interpretations about the content withinindividual and sets of objects 232 using statistical pattern matching ofwords, phrases or representations, in written or audio format, or inpictorial format, within the content. Such statistical pattern matchingmay include, but is not limited to, semantic network techniques,Bayesian analytical techniques, neural network-based techniques, supportvector machine-based techniques, or other statistical analyticaltechniques. Relevant statistical techniques that may be applied by thepresent invention include those found in Vapnik, The Nature ofStatistical Learning Theory, 1999.

Adaptive Recommendations

As shown in FIG. 1, the adaptive system 100 generates adaptiverecommendations 250 using the adaptive recommendations function 240. Theadaptive recommendations 250, or suggestions, enable users to moreeffectively use and navigate through the adaptive system 100.

The adaptive recommendations 250 are presented as structural subsets ofthe structural aspect 210. FIG. 4 depicts a hypothetical structuralaspect 210, including a plurality of objects 212 and associatedrelationships 214. The adaptive recommendations function 240 generatesadaptive recommendations 250 based on usage of the structural aspect 210by the one or more users 200, possibly in conjunction withconsiderations associated with the structural aspect and the contentaspect.

Three structural subsets 280A, 280B, and 280C (collectively, structuralsubsets 280) are depicted. The structural subset 280A includes threeobjects 212 and one associated relationship, which are reproduced by theadaptive recommendations function 240 in the same form as in thestructural aspect 210 (objects are speckle shaded). The structuralsubset 280B includes a single object (object is shaded), with noassociated relationships (even though the object originally had arelationship to another object in the structural aspect 210).

The third structural subset 210C includes five objects (stripedshading), but the relationships between objects has been changed fromtheir orientation in the structural aspect 210. In the structural subset280C, a relationship 282 has been eliminated while a new relationship284 has been formed by the adaptive recommendations function 240. Thestructural subsets 280 depicted in FIG. 4 represent but three of amyriad of possibilities from the original network of objects.

The illustration in FIG. 4 shows a simplified representation ofstructural subsets 280 being generated from objects 212 andrelationships 214 of the structural aspect 210. Although not shown, thestructural subset 280 may also have corresponding associated subsets ofthe usage aspect 220, such as usage behaviors and usage behavioralpatterns. As used herein, references to structural subsets 280 are meantto include the relevant subsets of the usage aspect, or usage subsets,as well.

The adaptive recommendations 250 may be in the context of a currentlyconducted activity of the system 100, a currently accessed object 232,or a communication with another user 200. The adaptive recommendations250 may also be in the context of a historical path of executed systemactivities, accessed objects 212, or communications during a specificuser session or across user sessions. The adaptive recommendations 250may be without context of a current activity, currently accessed object212, current session path, or historical session paths. Adaptiverecommendations 250 may also be generated in response to direct userrequests or queries. Such user requests may be in the context of acurrent system navigation, access or activity, or may be outside of anysuch context.

Usage Behavior Categories

In Table 1, several different usage behaviors 270 identified by theadaptive system 100 are categorized. The usage behaviors 270 may beassociated with the entire user community, one or more sub-communities,or with individual users of the adaptive system 100.

TABLE 1 Usage behavior categories and usage behaviors. usage behaviorcategory usage behavior navigation and access activity, content andcomputer application accesses, including buying/selling paths ofaccesses or click streams subscription and personal or communitysubscriptions to self-profiling process topical areas interest andpreference self-profiling affiliation self-profiling (e.g., jobfunction) collaborative referral to others discussion forum activitydirect communications (voice call, messaging) content contributions orstructural alterations reference personal or community storage andtagging personal or community organizing of stored or tagged informationdirect feedback user ratings of activities, content, computerapplications and automatic recommendations user comments physicallocation current location location over time relative location tousers/object references

A first category of usage behaviors 270 is known as system navigationand access behaviors. System navigation and access behaviors includeusage behaviors 270 such as accesses to, and interactions with, objects212, such as activities, content, topical areas, and computerapplications. These usage behaviors may be conducted through use of akeyboard, a mouse, oral commands, or using any other input device. Usagebehaviors 270 in the system navigation and access behaviors category mayinclude, but are not limited to, the viewing or reading of displayedinformation, typing written information, interacting with online objectsorally, or combinations of these forms of interactions with the adaptivesystem 100.

System navigation and access behaviors may also include executingtransactions, including commercial transactions, such as the buying orselling of merchandise, services, or financial instruments. Systemnavigation and access behaviors may include not only individual accessesand interactions, but the capture and categorization of sequences ofobject accesses and interactions over time.

A second category of usage behaviors 270 is known as subscription andself-profiling behaviors. Subscriptions may be associated with specifictopical areas of the adaptive system 100, or may be associated with anyother structural subset 280 of the system 100. Subscriptions may thusindicate the intensity of interest (inferred interests 254) with regardto system objects 212, including specific topical areas. The delivery ofinformation to fulfill subscriptions may occur online, such as throughelectronic mail (email), on-line newsletters, XML feeds, etc., orthrough physical delivery of media.

Self-profiling refers to other direct, persistent (unless explicitlychanged by the user) indications explicitly designated by the one ormore users 200 regarding their preferences and interests, or othermeaningful attributes. The user 200 may explicitly identify interests oraffiliations, such as job function, profession, or organization, andpreferences, such as representative skill level (e.g., novice, businessuser, advanced). Self-profiling enables the adaptive system 100 to inferexplicit preferences 252. For example, a self-profile may containinformation on skill levels or relative proficiency in a subject area,organizational affiliation, or a position held in an organization.Self-profiling information may be used to infer preferences andinterests with regard to system use and associated topical areas, andwith regard to degree of affinity with other user community subsets. Theuser 200 may identify preferred methods of information receipt orlearning style, such as visual or audio, as well as relative interestlevels in other communities.

A third category of usage behaviors 270 is known as collaborativebehaviors. Collaborative behaviors are interactions among the one ormore users 200 of the adaptive system 100, or between users 200 andnon-system users. Collaborative behaviors may thus provide informationon areas of interest and intensity of interest. Interactions includingonline referrals of objects 212, such as through email, or structuralsubsets 280 of the system 100, whether to other system users 200 or tonon-users 260, are types of collaborative behaviors obtained by theadaptive system 100.

Other examples of collaborative behaviors include, but are not limitedto, online discussion forum activity, contributions of content or othertypes of objects 212 to the structural aspect 210 of the adaptive system100, or any other alterations of the structural aspect 210 for thebenefit of others. Collaborative behaviors may also include generaluser-to-user communications, whether synchronous or asynchronous, suchas email, instant messaging, interactive audio communications, anddiscussion forums, as well as other user-to-user communications that canbe tracked by the adaptive system 100.

A fourth category of usage behaviors 270 is known as referencebehaviors. Reference behaviors refer to the saving or tagging ofspecific objects 212 or structural subsets 280 of the system 100 by theuser 200 for recollection or retrieval at a subsequent time. The savedor tagged objects 212, or structural subsets 280, may be organized in amanner customizable by the user 200. The referenced objects 212(structural subsets 280), as well as the manner in which they areorganized by the user 200, may provide information on inferred interests254 and intensity of interest.

A fifth category of usage behaviors 270 is known as direct feedbackbehaviors. Direct feedback behaviors include ratings or otherindications of perceived quality by individuals of specific objects 212or their attributes. The direct feedback behaviors may reveal theexplicit preferences 252 of the user 200. In the adaptive system 100,the adaptive recommendations 250 produced by the adaptiverecommendations function 240 (see FIG. 1) may be rated. This enables adirect, adaptive feedback loop, based on explicit preferences 252specified by the user 200. Direct feedback also includes user-writtencomments and narratives associated with objects 212 in the system 100.

A sixth category of usage behaviors 270 is known as physical locationbehaviors. Physical location behaviors identify physical location andmobility behaviors of the user 200. Location of the user 200 may beinferred from, for example, information associated with a GlobalPositioning System or any other positionally aware system or device. Thephysical location of physical objects referenced by objects 212 may bestored in the system 100. Proximity of users 200 to other users 200, orto physical objects referenced by objects 212, may be inferred. Thelength of time, or duration, at which the user 200 resides in aparticular location may be used to infer intensity of interestsassociated with the particular location, or associated with objects 212that have a relationship to a physical location.

In addition to the usage behavior categories 246 depicted in Table 1,usage behaviors 270 may be categorized over time and across userbehavioral categories 246. Temporal patterns may be associated with eachof the usage behavioral categories 246. Temporal patterns associatedwith each of the categories may be tracked and stored by the adaptivesystem 100. The temporal patterns may include historical patterns,including how recently an object 212 is accessed. For example, morerecent behaviors may be inferred to indicate more intense currentinterest than less recent behaviors.

Another temporal pattern that may be tracked and contribute topreference inferences made is the duration associated with the access ofobjects 212, the interaction with the objects 212, or the user'sphysical proximity to objects 212 that refer to physical objects, or theuser's physical proximity to other users 200 of the adaptive system 100.For example, longer durations may generally be inferred to indicategreater interest than short durations. In addition, trends over time ofthe behavior patterns may be captured to enable more effective inferenceof interests and relevancy. Since adaptive recommendations 250 mayinclude a combination of structural aspects 210 and content aspects 230,the usage pattern types and preference inferencing may also apply tointeractions of the one or more users 200 with the adaptiverecommendations 250 themselves.

Adaptive System is Recursive and Iterative

FIG. 6 is a flow diagram depicting the processing flow of the adaptivesystem 100, to illustrate its iterative, recursive nature. Prior toinvoking the adaptive recommendations function 240 (see FIG. 1), one ormore users 200 will have used the adaptive system 100. At a first timefollowing usage (time n), the adaptive recommendations function 240 isinvoked (block 262). The adaptive recommendations function 240 mayautomatically or semi-automatically update the structural aspects 210 ofthe adaptive system 100 (block 264). The update may, for example,include a change to the relationship among objects 214.

At a subsequent time to the structural aspect update (time n+1), thesystem use 202 is captured by the adaptive system 100 (block 266).Recall that system use 202, or captured usage information 202, includesany interaction by the one or more users 200 of the adaptive system 100.The use of the system, and hence the captured usage information 202 maybe influenced by the updated structural aspects 210 from the previoustime period (time n).

As shown in FIG. 6, the adaptive recommendations function 240 may beiteratively invoked following each capture of the system use 202. Thus,at time n+2, the adaptive recommendations function 240 is invoked (block262), the adaptive recommendations being based on, among other things,the captured usage information 202 from the previous time period (timen+1). Based on the invocation of the adaptive recommendations function240, the structural aspect 210 may again be updated (block 264). Oncethe users 200 again use the adaptive system 100, the system use 202 iscaptured (block 266), such that the adaptive recommendations function240 can again be invoked. Thus, an iterative, feedback loop may beestablished between system usage 202 and the system structure (thestructural aspect 210), which may continue indefinitely.

Multiple invocations of the adaptive recommendations function 240 may berun, automatically or through direct user invocations, synchronously orasynchronously. Each invocation of the adaptive recommendations function240 performs one or more of the following: 1) providing adaptiverecommendations directly to individual users or to or groups of users(communities); 2) updating or modifying the system aspect 210; and, 3)updating or modifying the content aspect 230. The result of this processis multiple, distributed, feedback loops enabling adaptation of theadaptive system 100.

Public Information Framework

FIG. 7 depicts a framework 1100 that summarizes the use of individualand social information used by the adaptive system 100 to produceadaptive recommendations 250. The framework 1100 has analogies inevolutionary biology, see for example, Danchin et al, PublicInformation: From Nosy Neighbors to Cultural Evolution, Science, July2004.

Recall from FIG. 3C that usage behaviors 270 are part of the usageaspect 220 of the adaptive system 100. Usage behaviors 270 includecategorizations of system usage 202 over time and across usagecategories 246, whether at an individual user or community level. InFIG. 7, additional details associated with individual usage behaviors270 are depicted.

The individual usage behaviors 270 can be divided into private behaviors1120, and non-private behaviors 1130. Private behaviors 1120 arebehaviors of a user 200 that are unavailable to other users whilenon-private behaviors 1130 are behaviors that may be available to otherusers. As illustrated in FIG. 7, the non-private behaviors 1130 maybecome socially available information 1140.

The social information 1140 includes unintentional information orcommunications, or “cues” 1150, as well as intentional information orcommunications, or “signals” 1160. Cues 1150 may include by-productinformation from the intentional communications 1160, whether the cuesare derived by the user or users to whom the intentional communicationswere directed, or by a user or users other than to whom the intentionalcommunications were directed.

Recall from FIG. 1 that the adaptive recommendations function 240employs a preference inferencing algorithm 242 to derive explicitpreferences 252, inferred preferences 253, and inferred interests 254based on the captured usage information 202. As shown in FIG. 7,inferred preferences 253 and interests 254 are specifically derived fromsignals 1160 and cues 1150. The social information 1140 thus furtherincludes inferred preferences 253, such as reputations 253a, andinterests 254. Inferred preferences 253 and interests 254 may be formedfrom both signals 1160 and cues 1150, or from combinations thereof.

An added feature of the adaptive system 100 enables users to specify thelevel of privacy associated with the derivation of inferred preferences253 and interests 254. Users 200 may be able to adjust the level ofprivacy, through a privacy control 1152, associated with the privateinformation 1120 and non-private information 1130 being used by theadaptive system 100 to produce inferred preferences 253 and interests254. A privacy control 1152a allows the user to enable or disablenon-private cues 1150 and signals 1160 from being used to inferpreferences and interests. The adjusted level of privacy may be withregard to the tracking of, or the forming of inferences from, the cues1150 or the signals 1160, to beneficially adapt to the preferences ofthe user 200. Or, the adjusted level of privacy may be with regard tothe tracking of, or the forming of inferences from, the cues 1150 or thesignals 1160 that might be used by the adaptive system 100 to providemore effective adaptation to other user's requirements. In other words,the user 200 may choose to wholly or partially “opt out” of thepreference inferencing 242 performed by the adaptive system 100, withrespect to some or all of the usage behaviors 247 of the user 200.

Usage Framework

FIG. 8 depicts a usage framework 1000 for performing preferenceinferencing 242 of captured usage information 102 by the adaptive system100 of FIG. 1. The usage framework 1000 summarizes the manner in whichusage patterns 248 are managed within the adaptive system 100. Usagebehavior patterns 248 associated with an entire community, affinitygroup, or segment of users 1002 are captured by the adaptive system 100.In another case, usage patterns 248 specific to an individual, shown inFIG. 8 as individual usage patterns 1004, are captured by the adaptivesystem 100. Various sub-communities of usage may also be defined, as forexample sub-community A usage patterns 1006, sub-community B usagepatterns 1008, and sub-community C usage patterns 1010.

Memberships in the communities are not necessarily mutually exclusive,as depicted by the overlaps of the sub-community A usage patterns 1006,sub-community B usage patterns 1008, and sub-community C usage patterns1010 (as well as and the individual usage patterns 1004) in the usageframework 1000. Recall that a community may include a single user 200 ormultiple users. Sub-communities may likewise include one or more users200. Thus, the individual usage patterns 1004 in FIG. 8 may also bedescribed as representing the usage patterns of a community or asub-community. For the adaptive system 100, usage behavior patterns 248may be segmented among communities and individuals so as to effectivelyenable adaptive recommendations 250 for each sub-community orindividual.

The communities identified by the adaptive system 100 may be determinedthrough self-selection, through explicit designation by other users orexternal administrators (e.g., designation of certain users as“experts”), or through automatic determination by the adaptive system100. The communities themselves may have relationships between eachother, of multiple types and values. In addition, a community may becomprised not of human users, or solely of human users, but instead mayinclude one or more other computer-based systems, which may have reasonto interact with the adaptive system 100. Or, such computer-basedsystems may provide an input into the adaptive system 100, such as bybeing the output from a search engine. The interacting computer-basedsystem may be another instance of the adaptive system 100.

The usage behaviors 270 included in Table 1 may be categorized by theadaptive system 100 according to the usage framework 1000 of FIG. 8. Forexample, categories of usage behavior may be captured and categorizedaccording to the entire community usage patterns 1002, sub-communityusage patterns 1006, and individual usage patterns 1004. Thecorresponding usage behavior information 247 may be used to inferpreferences and interests at each of the user levels.

Multiple usage behavior categories 246 shown in Table 1 may be used bythe adaptive system 100 to make reliable inferences based on thepreferences, of the user 200 with regard to the content aspect 230 andthe structural aspect 210. There are likely to be different preferenceinferencing 242 results for different users 200. In addition, preferenceinferencing 242 may be different with regard to optimizing the contentaspect 230 for display to the user 200 versus inferred preferences thatare used for updating the structural aspect 210 or the content aspect230, as updates to the structural aspect 210 are likely to be persistentand affect many users.

As an example, simply using the sequences of content accesses as thesole relevant usage behavior on which to base updates to the structurewill generally yield unsatisfactory results. This is because thestructure itself, through navigational proximity, will create a tendencytoward certain navigational access sequence biases. Using just object orcontent access sequence patterns as the basis for updates to thestructural aspect 210 will therefore tend to reinforce the pre-existingstructure of the system 100, which may limit the adaptiveness of theadaptive system 100.

By introducing different or additional behavioral characteristics, suchas the duration of access of an object 212 or item of content(information 232), on which to base updates to the structural aspect 210of the system 100 (system structural updates), a more adaptive system isenabled. For example, duration of access will generally be much lesscorrelated with navigational proximity than access sequences will be,and therefore provide a better indicator of true user preferences.Therefore, combining access sequences and access duration will generallyprovide better inferences and associated system structural updates thanusing either usage behavior alone. Effectively utilizing additionalusage behaviors as described above will generally enable increasinglyeffective system structural updating. In addition, the adaptive system100 may employ user affinity groups to enable even more effective systemstructural updating than are available merely by applying eitherindividual (personal) usage behaviors or entire community usagebehaviors.

Furthermore, relying on only one or a limited set of usage behavioralcues 1150 and signals 1160 mitigates against potential “spoofing” or“gaming” of the system 100. “Spoofing” or “gaming” the adaptive system100 refers to conducting consciously insincere or otherwise intentionalusage behaviors 270 so as to influence the adaptive recommendations 250or changes to the structural aspect 210 by the adaptive system 100.Utilizing broader sets of system usage behavioral cues 1150 and signals1160 may lessen the effects of spoofing or gaming. One or morealgorithms may be employed to detect such contrived usage behaviors, andwhen detected, such behaviors may be compensated for by the preferenceand interest inferencing algorithm 242.

User Communities

As described above, the user 200 of the adaptive system 100 may be amember of one or more communities of interest, or affinity groups, witha potentially varying degree of affinity associated with the respectivecommunities. These affinities may change over time as interests of theuser 200 and communities evolve over time. The affinities orrelationships among users and communities may be categorized intospecific types. An identified user may be considered a member of aspecial sub-community containing only one member, the member being theidentified user. A user can therefore be thought of as just a specificcase of the more general notion of user segments, communities, oraffinity groups.

FIG. 9 illustrates the affinities among user communities and how theseaffinities may automatically or semi-automatically be updated by theadaptive system 100 based on user preferences which are derived fromsystem usage 202. An entire community 1000 is depicted in FIG. 9. Forthe adaptive system 100, the community may extend across organizationalor functional boundaries. The entire community 1000 extends acrossorganization A 1060 and organization B 1061. An “organization” may be abusiness, an institution, or any other collection of individuals. Theentire community 1000 includes sub-community A 1064, sub-community B1062, sub-community C 1069, sub-community D 1065, and sub-community E1067. A user 1063 who is not part of the entire community 1000 is alsofeatured in FIG. 9.

Sub-community B 1062 is a community which has many relationships oraffinities to other communities. These relationships may be of differenttypes and differing degrees of relevance or affinity. (The relationshipsbetween communities depicted in FIG. 9 are distinct from therelationships between objects 214 referred to in FIG. 3A.) For example,a first relationship 1066 between sub-community B 1062 and sub-communityD 1065 may be of one type, and a second relationship 1067 may be of asecond type. (In FIG. 9, the first relationship 1066 is depicted using adouble-pointing arrow, while the second relationship 1067 is depictedusing a unidirectional arrow.)

The relationships 1066 and 1067 may be directionally distinct, and mayhave an indicator of relationship or affinity associated with eachdistinct direction of affinity or relationship. For example, the firstrelationship 1066 has a numerical value 1068, or relationship value, of“0.8.” Several other relationship values are shown in FIG. 42, below,and are scaled to values between 0 and 1. The relationship value 1068thus describes the first relationship 1066 between sub-community B 1062and sub-community D 1065 as having a value of 0.8.

The relationship value may be scaled as in FIG. 9 (e.g., between 0 and1), or may be scaled according to another interval. The relationshipvalues may also be bounded or unbounded, or they may be symbolicallyrepresented (e.g., high, medium, low).

The user 1063, which could be considered a user community including asingle member, may also have a number of relationships to othercommunities, where these relationships are of different types,directions and relevance. From the perspective of the user 1063, theserelationship types may take many different forms. Some relationships maybe automatically formed by the adaptive system 100, for example, basedon interests or geographic location or similar traffic/usage patterns.Thus, for example the entire community 1000 may include users in aparticular city. Some relationships may be context-relative. Forexample, a community to which the user 1063 has a relationship could bejob-related and another community could be related to another aspect oflife, such as related to family, hobby, or health. Thus, sub-community E1067 may be the employees at a corporation to which the user 1063 has arelationship 1071; sub-community B 1062 may be the members of a sailingclub to which the user 1063 has a relationship 1073; sub-community C maybe the doctors at a medical facility to which the user 1063 has arelationship 1072. The generation of new communities which include theuser 1063 may be based on the inferred interests 254 of the user 1063 orother users within the entire community 1000.

Membership of communities may overlap, as indicated by sub-communities A1064 and C 1069. The overlap may result when one community is wholly asubset of another community, such as between the entire community 1000and sub-community B 1062. More generally, a community overlap will occurwhenever two or more communities contain at least one user in common.Such community subsets may be formed automatically by the adaptivesystem 100 based on preference inferencing 242 from usage patterns 248.For example, a subset of a community may be formed based on an inferenceof increased interest or demand of particular content or expertise of anassociated community. The adaptive system 100 is also capable ofinferring that a new community is appropriate. The adaptive system 100will thus create the new community automatically.

For each user, whether residing within, say, sub-community A 1064, orresiding outside the community 1000, such as the user 1063, therelationships (such as arrows 1066 or 1067), affinities, or“relationship values” (such as numerical indicator 1068), and directions(of arrows) are unique. Accordingly, some relationships (and specifictypes of relationships) between communities may be unique to each user.Other relationships, affinities, values, and directions may have moregeneral aspects or references that are shared among many users, or amongall users of the adaptive system 100. A distinct and unique mapping ofrelationships between users, such as is illustrated in FIG. 9, couldthus be produced for each user of the adaptive system 100.

The adaptive system 100 may automatically generate communities, oraffinity groups, based on user behaviors 270 and associated preferenceinferences 242. In addition, communities may be identified by users,such as administrators of the adaptive system 100. Thus, the adaptivesystem 100 utilizes automatically generated and manually generatedcommunities in generating adaptive recommendations 250.

The communities, affinity groups, or user segments aid the adaptivesystem 100 in matching interests optimally, developing learning groups,prototyping system designs before adaptation, and many other uses. Forexample, advanced users of the adaptive system 100 may receive a previewof a new adaptation of a system for testing and fine-tuning, prior toother users receiving this change.

The users 200 or communities may be explicitly represented as objects212 within the structural aspect 210 or the content aspect 230 of theadaptive system 100. This feature enhances the extensibility(portability) and adaptability of the adaptive system 100.

The user community structure depicted in FIG. 9 may be directly embeddedin the usage aspect 220. Further, the usage community structure and theusage aspect may be a fuzzy network-based structure. Fuzzy networks aredescribed in more detail, below.

Community Preference Inferences

The preferences of a given user community may be inferred from theamount of on-line traffic, or number of accesses or interactions,associated with individual objects 212, or with people or physicalobjects referenced by the object 212 (this may be termed, “popularity”).The users 200 may have the ability to subscribe to selected structuralsubsets 280 and assign degrees of personal interest associated with thestructural subsets, for the purposes of periodic updates on thestructural subsets. Recall that a structural subset is a portion orsubset of the structural aspect 210 of a system. The updates may beeffected through, for example, e-mail updates.

The relative frequency of structural subsets 280 (e.g., topics)subscribed to by the user community as a whole, or by selectedsub-communities, may be used to infer preferences at the community orsub-community level. The users 200 may create their own personalizedstructural aspect 210 through selection and saving of individual objects212 or multiple objects and optionally associated relationships or, moregenerally, structural subsets 280. In such embodiments, the relativefrequency of structural subsets being saved in the structural aspect 210of a particular user by the user community as a whole, or by selectedsub-communities, may also be used to infer community and sub-communitypreferences. These inferred community and sub-community preferences maybe derived directly from saved structural subsets 280, but also fromdirect or indirect affinities the saved structural subsets have withother structural subsets.

Users 200 of the adaptive system 100 may be able to directly ratestructural subsets 280 when they are accessed. In such embodiments, thepreferences of a community or sub-community may also be inferred throughratings of individual structural subsets. The ratings may apply againstboth the information 232 referenced by the structural subset 280, aswell as meta-information 234 such as an expert review of the informationreferenced by the system subset. Users 200 may have the ability tosuggest structural subsets 280 to one or more other users, andpreferences may be inferred from these human-based suggestions. Theinferences may be derived from correlating the human-based suggestionswith the inferred interests 254 of the receivers if the receivers of thehuman-based suggestions are users of the adaptive system 100 and have apersonal history of objects 212 viewed and/or a personal structuralaspect 210 that they may have created.

Expert Preference Inferences

In the adaptive system 100, community subsets, such as subject matterexperts, may be designated. Expert opinions on the relationship betweenobjects 212 may be encoded in the structural aspect 210 of the adaptivesystem 100. Expert views can be directly inferred from the structuralaspect. An expert or set of experts may directly rate individual objectsand expert preferences may be directly inferred from these ratings.

The history of access of objects 212 or associated meta-information 234by sub-communities, such as experts, may be used to infer preferences ofthe associated sub-community. Experts or other user sub-communities mayalso have the ability to create their own personalized structural aspect210 through selection and saving or tagging of objects 212. The relativefrequency of objects 212 being saved in personal structural aspects 210(such as a local hard drive) by experts or communities of experts mayalso be used to infer expert preferences. These inferences may bederived directly from saved or tagged objects 212, but also fromaffinities the saved objects have with other objects.

A sub-community may be generated by the adaptive system 100 to prototypea new set of adaptive recommendations 250. For example, a sub-communitymay reflect a newly optimized business process or a frequently traveledpath that many novice users of a larger community often follow. In suchcircumstances, the new set of adaptive recommendations 250 could beuseful as a learning tool for new users.

Personal Preference Inferences

Users 200 of the adaptive system 100 may subscribe to selectedstructural subsets 280 for the purposes of, for example, e-mail updateson these subsets. The objects 212 subscribed to by the user 200 may beused to infer the preferences of the user. Users 200 may create theirown personalized structural aspect 210 through selection and saving ortagging of objects 212. The relative frequency of objects 212 beingsaved in a personal structural aspect 210 by the user 200 may be used toinfer the individual preferences of the user. These inferences may bederived directly from saved objects 212, but also from direct orindirect affinities the saved objects have with other objects.

Users can also directly rate objects 212 when accessed. In suchembodiments, personal preferences may also be inferred through theseratings of individual objects 212. The ratings may apply against boththe information 232 referenced by the object 212, such as an expertreview of the information 232 referenced by the object 212. A personalhistory of paths of the objects 212 viewed may be stored. This personalhistory can be used to infer preferences of the user 200, as well astuning adaptive recommendations and suggestions by avoiding recommendingor suggesting objects 212 that have already been recently viewed orcompleted by the user 200.

Adaptive Recommendations and Suggestions

Adaptive recommendations 250 generated by the adaptive 30recommendations function 240 may combine inferences from community,sub-community (including expert), and personal behaviors andpreferences, as discussed above, to present to the one or more users200, one or more system structural subsets 280. The users 200 may findthe structural subsets particularly relevant given the currentnavigational context of the user within the system, the physicallocation of the user, and/or responsive to an explicit request of thesystem by the one or more users. In other words, the adaptiverecommendation function 240 determines preference “signals” from the“noise” of system usage behaviors.

The sources of user behavioral information, which typically include theobjects 212 referenced by the user 200, may also include the actualinformation 232 contained therein. In generating adaptiverecommendations 250, the adaptive system 100 may thus employ searchalgorithms that use text matching or more general statistical patternmatching to provide inferences on the inferred themes of the information232 embedded in, or referenced by, individual objects 212. Furthermore,the structural aspect 210 may itself inform the specific adaptiverecommendations 250 generated. For example, existing relationshipstructures within the structural aspect 210 at the time of the adaptiverecommendations 250 may be combined with the user preference inferencesbased on usage behaviors, along with any inferences based on the contentaspect 230 (the information 232).

Delivery of Adaptive Recommendations

FIG. 10 is a flow diagram showing how adaptive recommendations 250 aredelivered by the adaptive system 100. Recall from FIG. 1 that adaptiverecommendations 250 may be delivered directly to the one or more users200 (dotted arrow 255), may be used to automatically orsemi-automatically update the structural aspect 210 (dotted arrow 245)or the content aspect 230 (dotted arrow 246), or may be delivereddirectly to the non-user 260 of the adaptive system 100 (dotted arrow265).

The adaptive system 100 begins by determining the relevant usagebehavioral patterns 248 to be analyzed (block 283). The adaptive system100 thus identifies the relevant communities, affinity groups, or usersegments of the one or more users 200. Affinities are then inferredamong objects 212, structural subsets 280, and among the identifiedaffinity groups (block 284). This data enables the adaptiverecommendations function 240 to generate adaptive recommendations 250 ofthe one or more users 200 for delivery. The adaptive system 100 nextdetermines whether the adaptive recommendations 250 are to be deliveredto the recommendations recipients (e.g., users 200 or non-users 260), orare used to update the adaptive system 100 (block 285). Where therecommendations recipients are to receive the adaptive recommendations(the “no” prong of block 285), the adaptive recommendations 250 aregenerated based on mapping the context of the current system use (or“simulated” use if the current context is external to the actual use ofthe system) (block 286) to the usage behavior patterns 248 generated bythe preference inferencing algorithm 242 (block 286).

Adaptive recommendations are then delivered visually and/or in othercommunications forms, such as audio, to the recommendations recipients(block 287). The recommendations recipients may be individual users or agroup of users, or may be non-users 260 of the adaptive system 100. ForInternet-based applications, the adaptive recommendations 250 may bedelivered through a web browser directly, or through RSS/Atom feeds andother similar protocols.

The recommended structural subsets 280, along with associated contentmay constitute most or all of the user interface that is presented tothe recommendations recipient, on a periodic or continuous basis. Suchembodiments correspond to the continuous, fully adaptive interfacedescribed in the framework 2000 of FIG. 42, below, including systemswhich do not syndicate (2130), systems in which individual content issyndicated (2140), systems in which structural subsets are syndicated(2150), and systems which support recombinant structural syndication.

Where, instead, adaptive system 100 is to receive the adaptiverecommendations (the “yes” prong of block 285), the adaptiverecommendations 250 are used to update the structural aspect 210 or thecontent aspect 230. The adaptive recommendations 250 are generated basedon mapping the potential structural aspect 210 or content aspect 230 tothe affinities generated by the usage behavioral inferences (block 288).The adaptive recommendations 250 are then delivered to enable updatingof the structural aspect 210 or the content aspect 230 (block 289).

The adaptive recommendations function 240 may operate completelyautomatically, performing in the background and updating the structuralaspect 210 independent of human intervention. Or, the adaptiverecommendations function 240 may be used by users or experts who rely onthe adaptive recommendations 250 to provide guidance in maintaining thesystem structure as a whole, or maintaining specific structural subsets280 (semi-automatic).

The navigational context for the recommendation 250 may be at any stageof navigation of the structural aspect 210 (e.g., during the viewing ofa particular object 212) or may be at a time when the recommendationrecipient is not engaged in directly navigating the structural aspect210. In fact, the recommendation recipient need not have explicitly usedthe system associated with the recommendation 250.

Some inferences will be weighted as more important than other inferencesin generating the recommendation 250. These weightings may vary overtime, and across recommendation recipients, whether individualrecipients or sub-community recipients. As an example, characteristicsof objects 21 which are explicitly stored or tagged by the user 200 in apersonal structural aspect 210 would typically be a particularly strongindication of preference as storing or tagging system structural subsetsrequires explicit action by the user 200. The recommendationsoptimization algorithms 244 may thus prioritize this type of informationto be more influential in driving the adaptive recommendations 250 than,say, general community traffic patterns within the structural aspect210.

The recommendations optimization algorithm 244 will particularly try toavoid recommending objects 212 that the user is already familiar with tothe user. For example, if the user 200 has already stored or tagged theobject 212 in a personal structural subset 280, then the object 212 maybe a low ranking candidate for recommendation to the user, or, ifrecommended, may be delivered to the user with a designationacknowledging that the user has already saved or marked the object forfuture reference. Likewise, if the user 200 has recently already viewedthe associated system object (regardless of whether it was saved to hispersonal system), then the object would typically rank low for inclusionin a set of recommended objects.

The preference inferencing algorithm 242 may be tuned by the individualuser. The tuning may occur as adaptive recommendations 250 are providedto the user, by allowing the user to explicitly rate the adaptiverecommendations. The user 200 may also set explicit recommendationtuning controls to adjust the adaptive recommendations to her particularpreferences. For example, the user 200 may guide the adaptiverecommendations function 240 to place more relative weight on inferencesof expert preferences versus inferences of the user's own personalpreferences. This may particularly be the case if the user wasrelatively inexperienced in the corresponding domain of knowledgeassociated with the content aspect 230 of the system, or a structuralsubset 280 of the system. As the user's experience grows, she may adjustthe weighting toward inferences of the user's personal preferencesversus inferences of expert preferences.

Adaptive recommendations, which are structural subsets of the adaptivesystem 100 (sec FIG. 4), may be displayed in variety of ways to theuser. The structural subsets 280 may be displayed as a list of objects212 (where the list may be null or a single object). The structuralsubset 280 may be displayed graphically. The graphical display mayprovide enhanced information that may include depicting relationshipsamong objects (as in the “relationship” arrows of FIG. 9).

In addition to the structural subset 280, the recommendation recipientmay be able to access information 232 to help gain an understandingabout why the particular structural subset was selected as therecommendation to be presented to the user. The reasoning may be fullypresented to the recommendation recipient as desired by therecommendation recipient, or it may be presented through a series ofinteractive queries and associated answers, where the recommendationrecipient desires more detail. The reasoning may be presented throughdisplay of the logic of the recommendations optimization algorithm 244.A natural language (e.g., English) interface may be employed to enablethe reasoning displayed to the user to be as explanatory and human-likeas possible.

The personal preference of the user may affect the nature of the displayof the information. For example some users may prefer to see thestructural aspect in a visual, graphic format while other users mayprefer a more interactive question and answer or textual display.

System users may be explicitly represented as objects in the structuralaspect 210 and hence embodied in structural subsets 280. Either embodiedas structural subsets, or represented separately from structural subsets280, the adaptive recommendations 250 of some set of users of theadaptive system 100 may be determined and displayed to recommendationrecipients, providing either implicit or explicit permission is grantedby the set of users. The recommendations optimization algorithm 244 maymatch the preferences of other users of the system with the currentuser. The preference matches may include the characteristics ofstructural subsets stored or tagged by users, their structural subsetsubscriptions and other self-profiling information, and their systemusage patterns 248. Information about the recommended set of users maybe displayed. This information may include names, as well as otherrelevant information such as affiliated organization and contactinformation. The information may also include system usage information,such as common system objects subscribed to, etc. As in the case ofstructural subset adaptive recommendations, the adaptive recommendationsof other users may be tuned by an individual user through interactivefeedback with the adaptive system 100.

The adaptive recommendations 250 may be in response to explicit requestsfrom the user. For example, a user may be able to explicitly designateone or more objects 212 or structural subsets 280, and prompt theadaptive system 100 for a recommendation based on the selected objectsor structural subsets. The recommendations optimization algorithm 250may put particular emphasis on the selected objects or structuralsubsets, in addition to applying inferences on preferences from usagebehaviors, as well as optionally, content characteristics.

In some embodiments, the adaptive recommendations function 240 mayaugment the preference inferencing algorithm 242 with considerationsrelated to maximizing the revelation of user preferences, so as tobetter optimize the adaptive recommendations 250 in the future. In otherwords, where the value of information associated with reducinguncertainty associated with user preferences is high, the adaptiverecommendations function 250 may choose to recommend objects 212 orother recommended structural aspects 210 as an “experiment.” Forexample, the value of information will typically be highest forrelatively new users, or when there appears to be a significant changein usage behavioral pattern 248 associated with the user 200. Theadaptive recommendations function 240 may employ design of experiment(DOE) algorithms so as to select the best possible “experimental”adaptive recommendations, and to optimally sequence such experimentaladaptive recommendations, and to adjust such experiments as additionalusage behaviors 270 are assimilated. The preference inferencing 242 andrecommendations optimization 244 algorithms may also preferentiallydeliver content that is specially sponsored, for example, advertising orpublic relations-related content.

In summary, the adaptive recommendations 250 may be presented to theusers 200, to the non-user 260, or back to the adaptive system 100, forupdating either the structural aspect 210 or the content aspect 230. Theadaptive recommendations 250 will thus influence subsequent userinteractions and behaviors associated with the adaptive system 100,creating a dynamic feedback loop.

Automatic or Semi-Automatic System Structure Maintenance

The adaptive recommendations function 240, optionally in conjunctionwith system structure maintenance functions, may be used toautomatically or semi-automatically update and enhance the structuralaspect 210 of the adaptive system 100. The adaptive recommendationsfunction 240 may be employed to determine new relationships 214 amongobjects 212 in the adaptive system, within structural subsets 280, orstructural subsets associated with a specific sub-community. Theautomatic updating may include potentially assigning a relationshipbetween any two objects to zero (effectively deleting the relationshipbetween the two objects).

In either an autonomous mode of operation, or in conjunction with humanexpertise, the adaptive recommendations function 240 may be used tointegrate new objects 212 into the structural aspect 210, or to deleteexisting objects 212 from the structural aspect.

The adaptive recommendations function 240 may also be extended to scanand evaluate structural subsets 280 that have special characteristics.For example, the adaptive recommendations function 240 may suggest thatcertain of the structural subsets that have been evaluated arecandidates for special designation. This may include being a candidatefor becoming a new specially designated sub-system or structural subset.The adaptive recommendations function 240 will suggest to human users orexperts the structural subset 280 that is suggested to become a newsub-system or structural subset, along with existing sub-system orstructural subsets that are deemed to be “closest” in relationship tothe new suggested structural subset. A human user or expert may then beinvited to add the object or objects 212, and may manually createrelationships 214 between the new object and existing objects.

As another alternative, the adaptive recommendations function 240,optionally in conjunction with the system structure maintenancefunctions, may automatically generate the object or objects 212, and mayautomatically generate the relationships 214 between the newly createdobject and other objects 212 in the structural aspect 210.

This capability is extended such that the adaptive recommendationsfunction 240, in conjunction with system structure maintenancefunctions, automatically maintains the structural aspect and identifiedstructural subsets 280. The adaptive recommendations function 240 maynot only identify new objects 212, generate associated objects 212, andgenerate associated relationships 214 among the new objects 212 andexisting objects 212, but also identify objects 212 that are candidatesfor deletion. The adaptive recommendations function 240 may alsoautomatically delete the object 212 and its associated relationships214.

In this way the adaptive recommendations function 240, optionally inconjunction with a system structure maintenance function, mayautomatically adapt the structural aspect 210 of the adaptive system100, whether on a periodic or continuous basis, so as to optimize theuser experience.

In some embodiments, each of the automatic steps listed above withregard to updating the structural aspect 210 may be employedinteractively by human users and experts as desired.

Hence, the adaptive recommendations function 240, driven in part byusage behaviors, automatically or semi-automatically updates the systemstructural aspect 210 (see dotted arrow 245 in FIG. 1). The feedbackloop is closed as user interactions with the adaptive system 100 areinfluenced by the structural aspect 210, providing an adaptive,self-reinforcing feedback loop between the usage aspect 230 and thestructural aspect 210.

Automatic or Semi-Automatic System Content Maintenance

As shown in FIG. 1, the adaptive recommendations function 240 mayprovide the ability to automatically or semi-automatically update thecontent aspect 230 of the adaptive system 100 (see dotted arrow 246).Examples of content that may be updated include text, animation, audio,video, tutorials, manuals and interactive applications; reviews andbrief descriptions of the content may also be updated. Customized textor multi-media content suitable for online viewing or printing may begenerated. U.S. patent application Ser. No. 10/715,174 entitled “AMethod and System for Customized Print Publication and Management”discloses relevant approaches for updating the content aspect 230 and isincorporated here in its entirety by reference.

The adaptive recommendations function 240 may operate automatically,performing in the background and updating the content aspect 230independently of human intervention. Or, the adaptive recommendationsfunction 240 may be used by users 200 or special experts who rely on theadaptive recommendations 250 to provide guidance in maintaining thecontent aspect 230.

As in the case of the structural aspect 210, different communities mayalso be used to model the maintenance of the content aspect 230. Thecommunities, affinity groups, and user segments are used to adapt therelevancies and to create, alter or delete relationships 214 between theobjects 212. The adaptive recommendations 250 may present the objects212 to the user 200 in a different combination than initially may havebeen inputted and may treat sections of a larger object such as adocument, book or manual as multiple objects that can be recombined in apattern that is aligned with community usage, by creating or alteringrelationships between sections.

In addition, as user feedback on system activities and usage behavioralpatterns 248 is accumulated, the adaptive system 100 may suggest areaswhere extra content would be beneficial to users. For example, if theobject 212 is frequently rated by users 200 as difficult to understand,or if only expert users in a community are accessing the object, theadaptive system 100 may recognize the need for supplemental content(e.g., in the form of documentation or online tutorials ordemonstrations).

Hence, as shown in FIG. 1, the adaptive recommendations function 240,driven in part by usage behaviors 270, automatically orsemi-automatically updates the content aspect 230. The feedback loop isclosed as the interactions of the user 200 with the adaptive system 100are influenced by updates to the content aspect 230, providing anadaptive, self-reinforcing feedback loop between the usage aspect 210and the content aspect 230, and, in some embodiments, between the usageaspect 210, the structural aspect 220, and the content aspect 230.

Furthermore, the adaptive system 100 may serve as a “user” of anotheradaptive system. Recall from FIG. 1 that the one or more users 200 mayinclude a human entity, non-human entities, such as another computersystem, or a second adaptive system that interacts with the adaptivesystem. The second adaptive system is known herein as a virtual user ofthe adaptive system 100.

In FIG. 11, the one or more users 200A of adaptive system 100A have beenexpanded to include a human user 1206, a non-human user 1205, and avirtual user, adaptive system 100B. Interactions with the adaptivesystem 100A by each entity 100B, 1205, and 1206 are monitored and usedto make preference inferences. The interactions may include any of theusage behaviors 270 listed in Table 1. The interactions with theadaptive system 100A by the virtual user (adaptive system 100B) may comefrom the adaptive recommendations function 240B of the adaptive system100B, combined with functions suitable for interactions between the twosystems 100A and 100B. The adaptive recommendations function 240A maygenerate adaptive recommendations 250A to be received by any of therecommendations recipients, the human user 1206, the non-human computer1205, or the virtual user, the adaptive system 100B.

Where the adaptive system 100B is less “experienced” (relative to theadaptive system 100A), the adaptive recommendations function 240A mayserve as a training mechanism for the new adaptive system 100B. Given adistribution of objects 212 and their relationships 214, metrics andusage behaviors 270 associated with scope, subject and otherexperiential data such as patterns of other adaptive systems, theadaptive recommendations function 240A may automatically beginassimilation of objects 212 into the less experienced adaptive system100B, possibly with intervention by human users. Clusters of newlyassimilated objects 212 may enable inferences resulting in thesuggestion of new structural subsets 280, communities; and theirassociated relationships would also be, in some embodiments,automatically created and updated. Application of mutual trainingfunctionality of the adaptive recommendation engine may also be appliedwhen two or more adaptive systems are directly integrated.

The virtual user (adaptive system 100B) may be integrated with human andnon-human users, as depicted in FIG. 11, or the virtual user may besegregated from other users 200 of the adaptive system 100, as desired.As in the case of the human user 1206, the virtual user 100B may beexplicitly represented as an object 212 within the adaptive system 100A,as shown in FIG. 12. Thus, any of the users 200A, human user 1206,non-human user 1205, or virtual user 100B may be explicitly representedas information 232 within the content aspect 230A (and associated object212 in the structural aspect 210A) of the adaptive system 100A. In thisway, the content aspect 230A may be extended to encompass users 200 ofthe adaptive system 200A. Thus, the users 200A of the adaptive system100A are merged, in a representational sense, with the adaptive systemitself. The representation of users 200 as being part of the content230A, as shown in FIG. 12, reflects aspects of social networks andadaptive systems that are beneficially combined.

As with the human user 1206 and the non-human user 1205, virtual usersmay mutually “use” or interact with one another, as represented by thearrows 201, 203, and 205 leading from the users 200 and the dotted arrow255 leading from the adaptive recommendations 250A to the users 200B.The mutual interaction between the adaptive systems 100A and 100B enablecollective evolution of the structural aspects 210A and 210B and thecontent aspects 230A and 230B. This principle may be extended tomultiple adaptive systems mutually interacting with one another.

The adaptive system 100 is distinguishable from collaborativefiltering-based prior art. For example, U.S. Pat. No. 5,790,426,entitled “Automated Collaborative Filtering System” (Robinson)recommends information items based on direct ratings of multiple systemusers. However, among many other aspects of distinction, the Robinsoninvention is limited to inferences associated with one type of usagebehavior, the direct rating of informational items only, and has noprovisions for modifying the system structure or content based onpreference inferences.

Network-Based Embodiments

The structural aspect 210 of the adaptive system 100 may be based on anetwork structure. The structural aspect 210 thus includes two or moreobjects, along with associated relationships among the objects.Networks, as used herein, are distinguished from other structures, suchas hierarchies, in that networks allow potential relationships betweenany two objects of a collection of objects. In a network, there are notnecessarily well-defined parent objects, and associated children,grandchildren, etc., objects, nor a “root” object associated with theentire system, as there would be by definition in a hierarchy. In otherwords, networks may include cyclic relationships that are not permittedin strict hierarchies. As used herein, a hierarchy can be thought of asjust one particular form of a network, with some additional restrictionson relationships among network objects.

The adaptive system 100 is distinguishable from network-based systemstructures of the prior art. For example, U.S. Pat. No. 6,285,999,entitled “Method for Node Ranking in a Linked Database” (Page), is alinked node search algorithm that presents a ranking of nodes based onthe relative level of linkages among the nodes. However, among manyother aspects of distinction, the Page invention is limited to non-fuzzynetworks, does not generate persistent structural or contentmodifications, and does not utilize system usage information as does theadaptive system 100. Another example, U.S. Pat. No. 5,875,446, entitled“System and Method for Hierarchically Grouping and Ranking a Set ofObjects in a Query Context Based on One or More Relationships” (Brown,et al), delivers a retrieved set of objects from an object base that haspotentially non-directed, weighted relationships, and organizes theretrieved objects in a hierarchical structure. However, among many otheraspects of distinction, the Brown, et al, invention does not generatepersistent structural or content modifications, does not enable deliveryof non-hierarchical structures to users, and does not utilize systemusage information, as does the adaptive system 100.

The structural aspect 210 of the adaptive system 100 may also have afuzzy network structure. Fuzzy networks are distinguished from othertypes of network structures in that the relationships between objects infuzzy networks may be by degree. In non-fuzzy networks, therelationships between objects are binary. Thus, between any two objects,relationships either exist or they don't exist.

As used herein, a fuzzy network is defined as a network of informationin which each individual item of information may be related to any otherindividual item of information, and the associated relationship betweenthe two items may be by degree. A fuzzy network can be thought ofabstractly as a manifestation of relationships among fuzzy sets (ratherthan classical sets), hence the designation “fuzzy network.” As usedherein, a non-fuzzy network is a subset of a fuzzy network, in whichrelationships are restricted to binary values (i.e., relationship eitherexists or does not exist). Pedrycz and Gomide, Introduction to FuzzySets: Analysis and Design, 1998 provide additional background regardingfuzzy sets.

Generalizing further, both classical networks and fuzzy networks mayhave a-directional (also called non-directed) or directed links betweennodes. Four network topologies are listed in Table 2.

TABLE 2 Network Topologies network type links between nodes link typetype i (classical) binary a-directional type ii (classical) binarydistinctly directional type iii (fuzzy) multi-valued a-directional typeiv (fuzzy) multi-valued distinctly directional The first two types (iand ii) are classical networks. Fuzzy networks, as used herein, arenetworks with topologies iii or iv.

For each of the four network topologies listed in Table 2, anotherpossible variation exists: whether the network allows only a single linkor multiple links between any two nodes, where the multiple links maycorrespond to multiple types of links. For example, the fuzzy networktypes (iii and iv) of Table 2 may permit multiple directionally distinctand multi-valued links between any two nodes in the network. Theadaptive system 100 encompasses any of the network topologies listed inTable 2, including those which allow multiple links and multiple linktypes between nodes.

The relationship among nodes in a fuzzy network may be described mostgenerally by an affinity matrix. For a network with N nodes, n₁ . . .n_(i), for integer i, the affinity matrix will have N rows and columns.Each cell of the matrix contains a number from 0 to 1 that describes therelationship between the associated two nodes, n_(a) and n_(b), 1≦a,b≦i.For classic networks (topology i or ii), each cell of the affinitymatrix contains either a 0 or a 1; for fuzzy networks (topology iii oriv), each cell, when normalized, contains a number between 0 and 1,inclusive. If the network allows multiple types of links between any twonodes, then each type of link will have a corresponding affinity matrixassociated therewith.

It is instructive to review networks that are familiar and theirassociated topologies. For example, the World Wide Web, which has beenmuch studied, is generally thought of as a directionally distinct,binary link network (topology ii). In other words, either a web page hasa link to another web page or it does not, and the link between the webpage and the other web page has a particular direction. (Although theremay be multiple links between two web pages, the links are not differentin link type, in that they do not have distinctive relationshipmeanings. The brain, on the other hand, seems to be a fuzzy network, andthe links between neurons seem to be generally directionally distinct(Laughlin and Sejnowski, Communication in Neuronal Networks, Science,September 2003). Social networks also seem to be fuzzy networks, and tothe links among people may sometimes be modeled as a-directional, butmore descriptively may be modeled as directionally distinct.

Mathematically, for a non-fuzzy network, it can be said, without loss ofgenerality, that a relationship translates to either a “0” or a “1”—“0,”for example if there is not a relationship, and “1” if there is arelationship. For fuzzy networks, the relationships between any twonodes, when normalized, may have values along a continuum between 0 and1 inclusive, where 0 implies no relationship between the nodes, and 1implies the maximum possible relationship between the nodes.Fundamentally then, fuzzy networks can provide more information aboutthe relationship among network nodes than can non-fuzzy networks.

FIG. 13A depicts a non-fuzzy, a-directional network 300 (topology i)according to the prior art, in which up to one relationship type betweennodes is possible. Two nodes, Node Y and Node Z have a relationship 305,as designated by the line between the two nodes. The relationship 305 isassumed to be bi-directional, as there is not sufficient information ina non-directed relationship to assume otherwise. The value of therelationship is represented by the relationship indicator 307. For NodeZ and Node X, there is no direct relationship, and therefore no line orassociated relationship indicator between the two nodes. Alternatively,a line could be drawn between Node Z and Node X, with an associatedrelationship indicator of “0” to represent a null relationship betweenthe two nodes.

FIG. 13B depicts a non-fuzzy a-directional network 110 (topology i)according to the prior art, in which multiple relationship types betweenat least two nodes in the network is possible. Two distinct types ofrelationships 312 and 314 are shown between Node V and Node W. Arelationship 309 (having a value of “1”) is associated with therelationship type 312 while a relationship 311 (having a value of “1”)is associated with the relationship type 314. Again, where norelationship exists between two nodes, such as Node X and Node W, a linewith an associated relationship value of “0” may be included in thediagram.

FIG. 14A illustrates how a non-fuzzy, and thus implicitly bidirectionalrelationship, may be decomposed into two separate directed relationships(topologies i and ii), according to the prior art. In the two-nodenetwork 320, there exists a relationship 322 between Node A and Node B,with a corresponding relationship indicator 323 with a value of “1.” Thesame network 320 can be alternatively depicted as having two directedrelationships, relationship 326 and relationship 328 between Node A andNode B, with corresponding relationship indicators optionally shown andset to “1,” by definition.

FIG. 14B illustrates the same alternative representations ofbidirectional relationships for fuzzy networks (topologies iii and iv),according to the prior art. Fuzzy network 330 is comprised of two nodes,Node C and Node D, and a relationship designator 331 between the twonodes. The relationship is bi-directional, as signified by the dualarrows associated with 331, and with an asymmetry of relationshipbetween the two nodes, as indicated by the distinct and unequalrelationship indicators 332 and 334 associated with 331. An alternativerepresentation of the same fuzzy network 330 decomposes relationship 331into two separate directionally distinct relationship designators 336and 338, with associated relationship indicators 337 and 339.

FIGS. 15A and 15B depict a directed, non-fuzzy analog to thenon-directed, non-fuzzy network examples illustrated by FIGS. 13A and13B, according to the prior art. FIG. 15A depicts a non-fuzzy,non-directed network 340 (topology i). A unidirectional directedrelationship 342 is shown going from Node E to Node F with an associatedrelationship indicator 344. Relationship indicators are by definition“1” for any non-null relationship in a non-fuzzy network and need nottherefore in general be explicitly shown as they are in FIG. 15A.Relationship 346 depicts a bidirectional relationship between Node E andNode G.

FIG. 15B depicts a directed, non-fuzzy network 350 with multiplerelationship types between at least two nodes in the network (topologyii), according to the prior art. As an example, two distinct types ofrelationships 352 and 354 are shown between Node H and Node J.

FIGS. 16A and 16B depict a-directional fuzzy networks (topology iii),according to the prior art. The network 360 in FIG. 13A includes arelationship 364 between Node M and Node N that has an associatedrelationship indicator 366 with a value of 0.4. A different relationshipindicator 368 is included between Node M and Node P. The relationshipindicator 368 has a value of “1,” indicating the closest possiblerelationship (e.g., the identity relationship) between nodes. FIG. 16Balso depicts an a-directional fuzzy network 370, this time with multiplerelationship types between at least two nodes. Two distinct types ofrelationships 172 and 174 are shown between Node Q and Node R.

FIGS. 17A and 17B depict directed fuzzy networks (topology iv),according to the prior art. The network 380 in FIG. 14A includes arelationship 382 between Node S and Node T that has an associatedrelationship indicator 384. A different relationship indicator 386between Node S and Node U depicts a situation where the relationshipvalue and associated indicator may equal “1,” meaning, depending oncontext, the closest possible relationship (e.g., the identityrelationship). FIG. 17B depicts a non-directed fuzzy network 390 withmultiple relationship types between at least two nodes in the network.As an example, two distinct types of relationships 392 and 394 are shownbetween Node V and Node W.

The structural aspect 210 of the adaptive system 100 of FIG. 1 maysupport any of the network topologies described above. A-directionalrelationships between nodes (no arrows), directed relationships betweennodes (whether single- or double-arrow), and multiple types ofrelationships between nodes, are supported by the adaptive system 100.Further, relationship indicators which are binary (e.g., 0 or 1) ormulti-valued (e.g., range between 0 and 1) are supported by the adaptivesystem.

It can readily be seen that a hierarchy may be described as a directedfuzzy network with the additional restrictions that the relationshipvalues and indicators associated with each relationship must be either“1” or “0” (or the symbolic equivalent). Further, hierarchies do notsupport cyclic or closed relationship paths.

Although the network structures and variations described herein arerepresented in the accompanying figures by a network pictorial style, itshould be understood that some embodiments may use alternativerepresentations of network structures. These representations may includeaffinity matrices, as described herein, tabular representations, vectorrepresentations, or functional representations. Furthermore, the networkoperators and algorithms described herein may operate on any of theserepresentations, or on combinations of network representations.

In FIG. 18, according to sonic embodiments, an adaptive recombinantsystem 800 is depicted. The adaptive recombinant system 800 includes theadaptive system 100 of FIG. 1, as well as a syndication function 810, afuzzy network operators function 820, and an object evaluation function830. The adaptive recombinant system is capable of syndicating andrecombining structural subsets 280. The structural subsets 280 may bederived through either direct access of the structural aspect 210 by thefuzzy network operators function 820, or the structural subsets 280 maybe generated by the adaptive recommendations function 240. The adaptiverecombinant system 800 of FIG. 18 is capable of syndicating (sharing)and recombining the structural subsets, whether for display to the user200 or non-user 260, or to update the structural aspect 210 and/or thecontent aspect 230 of the adaptive system 100. In addition, thesefunctions are capable of updating multiple adaptive systems, or aidingin the generation of a new adaptive system.

The syndication function 810 may syndicate elements of the usage aspect220 associated with syndicated structural subsets 280, thus enablingelements of the usage clusters and patterns, along with thecorresponding structural subsets, to be combined with other structuralsubsets and associated usage clusters and patterns.

As explained above, the structural aspect 210 of the adaptive system 100employs a network structure, and is not restricted to a particular typeof network. In some embodiments, the adaptive recombinant system 800operates on an adaptive system in which the structural aspect 210 is afuzzy network. The structural subsets 280 generated by the adaptiverecombinant system 800 during syndication or recombination are likewisefuzzy networks in these embodiments, and are also called adaptiverecombinant fuzzy networks. Recall that a structural subset is a portionor subset of the structural aspect 210 of the adaptive system 100. Thestructural subset 280 may include a single or multiple objects, andtheir associated relationships.

Generalized Network Degrees of Separation

The notion of the degree of separation among nodes in non-fuzzy networksis well known. Degrees of separation may be employed as a metric todescribe a “neighborhood” within a network. The degree of separationbetween any two nodes is defined as the shortest path between the twonodes. For networks with directionally distinct relationships betweennodes, the shortest path between any two nodes may be specified toadhere to a specific directional orientation.

A node can be thought of as having a zeroth degree of separation withitself. The node has a first degree of separation from other nodes towhich it is directly connected. The node has a second degree ofseparation from the nodes that are directly connected to first degree ofseparation nodes and are not already more closely separated, and so on.FIG. 21 depicts a non-fuzzy, a-directional network 600 and theassociated degrees of separation 602 from Node X, according to the priorart.

The notion of degrees of separation of non-fuzzy networks is extended tofuzzy networks in the adaptive system 100. Fractional degrees ofseparation among nodes may be attributed to fuzzy networks. The degreeof separation between the two nodes can be defined as:(scaling factor+(1−affinity_(ij)))for a given affinity level, affinity_(ij), where 0<affinity_(ij)≦1, forNode i and Node j, and where 1 is the strongest possible relationship,excluding the identity relationship, and 0 implies no directrelationship. “Scaling factor” is a number between 0 and 1 chosen tonormalize the degrees of separation for the fuzzy network consistentwith the specific definition and distributions of the affinities betweennodes in the fuzzy network.

For example, if an affinity of 1.0 is defined as the identity function,then the scaling factor could be set to 0 so that the degree ofseparation of an affinity of 1.0, the identity degree of separation, isdefined as 0. Alternatively, if an affinity of 0 is defined as norelationship whatsoever, then the degree of separation should logicallybe greater than 1.0, so the scaling factor may be chosen as a number upto and including 1.0.

The scaling factor may be a function of the specific distribution of theintensity level of affinities in a fuzzy network. These intensities maybe linear across the range of 0 and 1, or may be nonlinear. If, forexample, the mean intensity is defined at 0.5, then the scaling factorfor the fractional degree of separation calculation could be set at 0.5.

In summary, for fuzzy networks, the general case of “distance”relationship between two directly linked nodes is a fractional degree ofseparation. More generally, the degree of separation between any twonodes in a fuzzy network is defined as the minimum of the degrees ofseparation (which may be calculated on the basis of a specificdirectional orientation of relationships among the nodes) among allpossible paths between the two nodes, where the degrees of separationbetween any two nodes along the path may be fractional. Where a networkhas multiple relationships between nodes, multiple potentiallyfractional degrees of separation may be calculated between any two nodesin the network.

For convenience, the term fractional degrees of separation may beshortened to the acronym “FREES” (FRactional degrEEs of Separation)—asin, say, “Node X is 2.7 FREES from Node Y.” FIG. 22 represents a fuzzy,a-directional network 610 and the associated degrees of separation 622(using a scaling factor of 0.5) from Node X.

The degree of separation within the fuzzy or non-fuzzy network may becalculated and displayed on demand for any two nodes in the network. Allnodes within a specified degree of separation of a specified node may becalculated and displayed. Optionally, the associated fractional degreesof separation between the base node and the nodes within the specifiedfractional degrees of separation may be displayed.

FIG. 23 depicts a subset 620 of the non-fuzzy a-directional network 600of FIG. 21, according to the prior art, where the subset 620 is definedas all nodes within two degrees of separation of Node X. FIG. 24 depictsa subset 630 of the fuzzy a-directional network 610 of FIG. 22,according to some embodiments, where the subset 630 is defined as allnodes within 2.5 degrees of separation of Node X.

The degrees of separation among nodes in a fuzzy network may bedescribed by a fractional degrees-of-separation (FREES) matrix. For anetwork with N nodes, n₁ . . . n_(un), the degree-of-separation matrixwill have N rows and columns. Each cell of the matrix contains a numberthat describes the degree of separation between the associated twonodes, i_(n) and n_(o). For non-fuzzy networks, each cell will containan integer value; for fuzzy networks each cell of the FREES matrix maycontain non-integer values. For both fuzzy and non-fuzzy networks, thediagonal of the affinity matrix will be 0's—the identity degree ofseparation. If a fuzzy network is described by multiple affinitymatrices, then the multiple affinity matrices correspond on a one-to-onebasis with multiple associated FREES matrices.

The degrees of separation for networks with multiple relationship types,whether for fuzzy or non-fuzzy networks, may be calculated as a functionacross some or all of the relationship types. For example, such afunction could be the minimum of degree of separation from Node X toNode Y of all associated relationship types, or the function could be anaverage, or any other relevant mathematical function.

According to some embodiments, the adaptive recombinant system 800 ofFIG. 18 employs fractional degrees of separation in its syndication andrecombination operations, as described in more detail, below.

Fuzzy Network Subsets and Adaptive Operators

The adaptive recombinant system 800 of FIG. 18 includes fuzzy networkoperators 820. The fuzzy network operators 820 may manipulate one ormore fuzzy or non-fuzzy networks. Some of the operators 820 mayincorporate usage behavioral inferences associated with the fuzzynetworks that the operators act on, and therefore these operators may betermed “adaptive fuzzy network operators.” The fuzzy network operators820 may apply to any fuzzy network-based system structure, includingfuzzy content network system structures, described further below.

FIG. 20 is a block diagram depicting some fuzzy network operators 820,also called functions or algorithms, used by the adaptive recombinantsystem 800. A selection operator 822, a union operator 824, anintersection operator 826, a difference operator 828, and a complementoperator 832 are included, although additional logical operations may beused by the adaptive recombinant system 800. Additionally, the fuzzynetwork operators 820 include a resolution function 834, which is usedin conjunction with one or more of the operators in the fuzzy networkoperators 820.

A selection operator 822, which selects subsets of networks, maydesignate the selected network subsets based on degrees of separation.For example, subsets of a fuzzy network may be selected from theneighborhood, designated by a FREES metric, around a given node, sayNode X. The selection may take the form of selecting all nodes withinthe designated network neighborhood, or all the nodes and all theassociated links as well within the designated network neighborhood,where the network neighborhood is defined as being within a certaindegree of separation from Node X. A non-null fuzzy network subset willtherefore contain at least one node, and possibly multiple nodes andrelationships.

Two or more fuzzy network subsets may then be operated on by networkoperations such as union, intersection, difference, and complement, aswell as any other Boolean set operators. An example is an operation thatoutputs the intersection (intersection operator 826) of the networksubset defined by the first degree or less of separation from Node X andthe network subset defined by the second or less degree of separationfrom Node Y. The operation would result in the set of nodes andrelationships common to these two network subsets, with specialauxiliary rules optionally applied to resolve duplicative relationshipsas will be explained below.

The network operations may apply explicitly to fractional degrees ofseparation. For example, the union operator 824 may be applied to thenetwork subset defined by half a degree of separation (0.5) or less fromNode X and the network subset defined as 2.4 degrees of separation orless from Node Y. The union of the two network subsets results in aunique set of nodes and relationships that are contained in both ofthese network subsets. Special auxiliary rules may optionally be appliedto resolve duplicative relationships. Fuzzy network operations may alsobe chained together, e.g., a union of two network subsets intersectedwith a third network subset, etc.

The fuzzy network operators 820 may have special capabilities to resolvethe situation in which union 824 and intersection 826 operators definecommon nodes, but with differing relationships or values of therelationships among the common nodes. The fuzzy network intersectionoperator 826, Fuzzy_Network_Intersection, may be defined as follows:Z=Fuzzy_Network_Intersection(X, Y, W)where X, Y, and Z are network subsets and W is the resolution function834. The resolution function 834 designates how duplicativerelationships among nodes common to fuzzy network subsets X and Y areresolved.

Specifically, the fuzzy network intersection operator 826 firstdetermines the common nodes of network subsets X and Y, to form a set ofnodes, network subset Z. The fuzzy network intersection operator 826then determines the relationships and associated relationship value andindicators uniquely deriving from X among the nodes in Z (that is,relationships that do not also exist in Y), and adds them into Z(attaching them to the associated nodes in Z). The operator thendetermines the relationships and relationship indicators and associatedvalues uniquely deriving from Y (that is, relationships that do not alsoexist in X) and applies them to Z (attaching them to the associatednodes in Z).

For relationships that are common to X and Y, the resolution function834, is applied. The resolution function 834 may be any mathematicalfunction or algorithm that takes the relationship values of X and Y asarguments, and determines a new relationship value and associatedrelationship indicator.

The resolution function 834, Resolution_Function may be a linearcombination of the corresponding relationship value of X and thecorresponding relationship value of Y, scaled accordingly. For example:Resolution_Function (X_(RV), Y_(RV))=(c₁*X_(RV)+c₂*Y_(RV))/(c₁+c₂)where X_(RV) and Y_(RV) are relationship values of X and Y,respectively, and c₁ and c₂ are coefficients. If c₁=1, and c₂=0, thenX_(RV) completely overrides Y_(RV). If c₁=0 and c₂=1, then Y_(RV)completely overrides X_(RV). If c₁=1 and c₂=1, then the derivedrelationship is a simple average of X_(RV) and Y_(RV). Other values ofc₁ and c₂ may be selected to create weighted averages of X_(RV) andY_(RV). Nonlinear combinations of the associated relationships values,scaled appropriately, may also be employed.

The Fuzzy_Network_Union operator 824 may be derived from theFuzzy_Network_Intersection operator 826, as follows:Z=Fuzzy_Network_Union(X, Y, W)where X, Y and Z are network subsets and W is the resolution function834. Accordingly,Z=Fuzzy_Network_Intersection(X, Y, W)+(X−Y)+(Y−X)That is, fuzzy network unions of two network subsets may be defined asthe sum of the differences of the two network subsets (the nodes andrelationships that are uniquely in X and Y, respectively) and the fuzzynetwork intersection of the two network subsets. The resulting networksubset of the difference operator contains any unique relationshipsbetween nodes uniquely in an originating network subset and the fuzzynetwork intersection of the two subsets. These relationships are thenadded to the fuzzy network intersection along with all the unique nodesof each originating network subset, and all the relationships among theunique nodes, to complete the resulting fuzzy network subset.

It should be noted that, unlike the corresponding classic set operators,the fuzzy network intersection 826 and union 824 operators are notnecessarily mathematically commutative—that is, the order of theoperands may matter. The operators will be commutative if the resolutionfunction or algorithm is commutative.

For the adaptive recombinant system 800, the resolution function 834that applies to operations that combine multiple networks mayincorporate usage behavioral inferences related to one or all of thenetworks. The resolution function 834 may be instantiated directly bythe adaptive recommendations function 240 (FIG. 18), or the resolutionfunction 834 may be a separate function that invokes the adaptiverecommendations function. The resulting relationships in the combinednetwork will therefore be those that are inferred by the system to bestreflect the collective usage histories and preference inferences of thepredecessor networks.

For example, where one of the predecessor networks was used by largernumbers of individuals, or by individuals that members of communities oraffinity groups that are inferred to be best informed on the subject ofthe associated content, then the resolution function 834 may choose topreferentially weight the relationships of that predecessor networkhigher versus the other predecessor networks. The resolution function834 may use any or all of the usage behaviors 270, along with associateduser segmentations and affinities obtained during usage behaviorpre-processing 204 (see FIG. 3C), as illustrated in FIG. 8 and Table 1,and combinations thereof, to determine the appropriate resolution ofcommon relationships and relationship values among two or more networksthat are combined into a new network.

Fuzzy Network Metrics

Special metrics may be used to measure the characteristics of fuzzynetworks and fuzzy network subsets. For example, these metrics mayprovide measures associated with the relationship of a network node orobject to other parts of the network, and relative to other networknodes or objects. A metric may be provided that indicates the degree towhich nodes are connected to the rest of the network. This metric may becalculated as the sum of the affinities of first degree or lessseparated directionally distinct relationships or links. The metric maybe called a first degree connectedness parameter for the specific node.

The first degree connectedness metric may be generalized for zeroth toN^(th) degrees of connectedness as follows. The zeroth degree ofconnectedness is, by definition, zero. The N^(th) degree ofconnectedness of Node X is the sum of the affinities among all nodeswithin N degrees of separation of Node X. For fuzzy networks, N may notnecessarily be an integer value. The connectedness parameters may beindexed to provide a convenient relative metric among all other nodes inthe network.

As an example, in the fuzzy network 630 of FIG. 24, the first degree ofconnectedness of Node X is determined by summing all relationship valuesassociated with Node X to objects within a fractional degree ofseparation, defined here as less than 1.5 degrees of separation. Fournodes which have less than 1.5 degrees of separation from Node X areshaded in FIG. 24. By summing the affinities of the four nodes(0.9+0.4+0.3+0.3), a connectedness metric of 1.9 for Node X is obtained.

In networks in which there are multiple types of relationships amongnodes, there may be multiple connectedness measures for any specificNode X to the subset of the fuzzy network specified by a degree ofseparation, N, from X.

In summary, connectedness for a specific Node X may have variationsassociated with relationship type, the specified directions of therelationships selected for computation, and the degree of separationfrom the Node X. The general connectedness metric function may bedefined as follows:Connectedness(Node X, T, D, S)where T is the relationship indicator type, D is the relationshipdirection, and S is the degree of separation. The Connectedness metricmay be normalized to provide a convenient relative measure by indexingthe metric across all nodes in a network.

A metric of the popularity of the network nodes or objects, orpopularity metric, may also be provided. The fuzzy or non-fuzzy networkmay be implemented on a computer system, or on a network of computersystems such as the Internet or on an Intranet. The system usagebehavioral patterns of users of the fuzzy network may be recorded. Thenumber of accesses of particular nodes or objects of a fuzzy tonon-fuzzy network may be recorded. The accesses may be defined as theactual display of the node or object to the user or the accesses may bedefined as the display of information associated with the node or objectto user, such as access to an associated editorial review. In some ofthese embodiments, the popularity metric may be based on the number ofuser accesses of the associated node or object, orassociated—information. The popularity metric may be calculated forprescribed time periods. Popularity may be recorded for various usersegments, in addition to, or instead of, the usage associated with theentire user community. The usage traffic may be stored so thatpopularity trends over time may be accessed. In the most general case,popularity for a specific Node X will have variations by user segmentsand time periods. A general popularity function may therefore berepresented as follows:Popularity(Node X, user segment, time period)The Popularity metric may be normalized to provide a convenient relativemeasure by indexing the metric across all nodes in a network.

Metrics may be generated that go beyond the connectedness metrics, toprovide information on additional characteristics associated with a nodeor object within the network relative to other nodes or objects in thenetwork. A metric that combines aspects of connectedness and popularitymeasures, an influence metric, may be generated. The influence metricmay provide a sense of the degree of importance or “influence” aparticular node or object has within the fuzzy network.

The influence metric for Node X is calculated by adding the popularityof Node X to a term that is the sum of the popularities of the nodes orobjects separated by one degree of separation or less from Node X,weighted by the associated affinities between Node X and each associatedrelated node. The term associated with the weighted average of thepopularities of the first degree of separation nodes of Node X is scaledby a coefficient. This coefficient may be defined as the inverse of thefirst degree connectedness metric of Node X.

For fuzzy networks with directionally distinct relationships andaffinities, the influence metric may be calculated based only on thefirst degree affinities or less for relationships that are oriented in aparticular direction. For example, influence may be calculated based onall relationships directed to Node X (as opposed to those directed awayfrom Node X).

A generalized influence metric may also be provided, where the N^(th)degree of influence of node or object X is defined as the popularity ofNode X added to a term that is the weighted average of the popularitiesof all nodes within N degrees of separation from Node X (where N may bea non-integer, implying a fractional degree of separation). The weightsfor each node may be a function of the affinities of the shortest pathbetween Node X and the associated node. The generalized influence metricmay be a multiplicative function, that is, the affinities along the pathfrom Node X to each node within N degrees separation are multipliedtogether and then multiplied by the popularity of the associated node.Or, the metric may be a summation function, or any other mathematicalfunction that combines the affinities along the associated network path.The generalized influence metric may be specified as a recursivefunction, satisfying the following difference equations and “initialcondition”:Nth Degree of Influence(Node X)=(N−1)th Degree of Influence(NodeX)+Influence of Nodes of N Degrees of Separation from Node X.   (1)Zeroth Degree of Influence(Node X)=Popularity(Node X)   (2)

Where there are directionally distinct affinities, the affinities thatare multiplied, summed, or otherwise mathematically operated on, betweenNode X and all other nodes within a directionally distinct degree ofseparation (where the degree of separation may be fractional), may be ofrelationships with a selected directional orientation. The relationshipdirection term (D, in the connectedness metric function, above, may bescaled by the N^(th) degree of connectedness (of a given directionalorientation) of Node X.

The zeroth degree of influence may be defined as just the popularity ofNode X. The N^(th) degree of influence is indexed to enable convenientcomparison of influence among nodes or objects in the network. Wherethere are multiple types of relationships between any two nodes in thenetwork, influence may be calculated for each type of relationship. Aninfluence metric may also be generated that averages (or applies anyother mathematical function that combines values) across multipleinfluence metrics associated with two or more relationship types.

FIG. 25 illustrates an example of influence calculations, using amultiplicative scaling method, in accordance with some embodiments.Fuzzy network 650 depicts Node X having a popularity metric 652 of “10”.The zeroth degree of influence of Node X is therefore just “10.” Thefirst degree of influence of Node X is calculated by multiplying theaffinities or relationship indicators associated with relationships fromNode X and nodes that are within one degree of separation, by theassociated popularities, for example 654, of these nodes. The firstdegree of influence of Node X is thus the popularity of Node X (10) plusthe sum of the popularities of the nodes within one degree ofseparation, multiplied by their associated relationship values. In FIG.25, the first degree of influence of Node X is:10+(45*0.3)+(23*0.9)+(85*0.4)+(42*0.3)=90.8

The second degree of influence of Node X is calculated as the firstdegree of influence of Node X (already calculated) plus the influencecontributed by each node that is two degrees of separation from Node X,and may likewise be calculated, as follows:90.8+(20*0.4*0.9)+(30*0.8*0.3)+(150*0.2*0.3)+(80*0.6*0.3)+(90*0.9*0.3)+(5*0.4*0.3)+(20*0.5*0.3)+(200*0.8*0.3)=204.5Table 3 lists the first degree affinities, second degree affinities,popularity, calculated influence, and cumulative influence, relative toNode X, for the fuzzy network 650 of FIG. 25.

TABLE 3 Affinity, popularity, & influence data for fuzzy network 650.cum. Node 1^(st) ° affinities 2^(nd) ° affinities popularity influenceinfluence 0^(th) 1 10 10 10 1^(st) 0.4 85 34 1^(st) 0.9 23 20.7 1^(st)0.3 42 12.6 1^(st) 0.3 45 13.5 90.8 2^(nd) 0.9 0.4 20 7.2 2^(nd) 0.3 0.830 7.2 2^(nd) 0.3 0.2 150 9 2^(nd) 0.3 0.9 90 24.3 2^(nd) 0.3 0.8 200 482^(nd) 0.3 0.5 20 3 2^(nd) 0.3 0.4 5 0.6 2^(nd) 0.3 0.6 80 14.4 204.5

In summary, the influence metric for Node X may have variationsassociated with a specific relationship indicator type, a specificdirection of relationships for the relationship indicator type, a degreeof separation from Node X, and a scaling coefficient that tunes thedesired degradation of weighting for nodes and relationshipsincreasingly distant from Node X. The metric function may therefore berepresented as follows:

Influence(Node X, relationship indicator type or types, relationshipdirection, degree of separation, affinity path function, scalingcoefficient). The influence metric may be normalized to provide aconvenient relative measure by indexing the metric across all nodes in anetwork. Metrics associated with nodes of fuzzy networks, such aspopularity, connectedness, and influence, may be displayed in textual orgraphical forms to users of the fuzzy network-based system. The adaptiverecombinant system 800 of FIG. 18 may use connectedness, popularity, andinfluence metrics in order to syndicate and recombine structural subsets280 of the adaptive system 100.

Fuzzy Network Syndication and Combination

The adaptive recombinant system 800 of FIG. 18 is able to syndicate andcombine structural subsets 280 of the structural aspect 210 (where astructural subset 280 may contain the entire structural aspect 210). Thestructural subsets 280, which are fuzzy networks, in some embodiments,may be syndicated in whole or in part to other computer networks,physical computing devices, or in a virtual manner on the same computingplatform or computing network. Although the adaptive recombinant system800 is not limited to generating structural subsets which are fuzzynetworks, the following figures and descriptions, used to illustrate theconcepts of syndication and recombination, feature fuzzy networks.Designers of ordinary skill in the art will recognize that the conceptsof syndication and recombination may be generalized to other types ofnetworks.

FIG. 26 illustrates a fuzzy network 500, including a subset 502 of fuzzynetwork 500. The subset 502 includes three objects 504, 506, and 508,designated as shaded in FIG. 26. The subset 502 also includes associatedrelationships (arrows) and relationship indicators (values) among thethree objects. The separated, or syndicated, subset of the network 502yields a fuzzy network (subset) 510.

The adaptive system 100 of FIG. 1 may operate in a fuzzy networkenvironment, such as the fuzzy network 500 of FIG. 26. In FIG. 27, anadaptive system 100C includes a structural aspect 210C that is a fuzzynetwork 500. Thus, adaptive recommendations 250 generated by theadaptive system 100C are also structural subsets that are themselvesfuzzy networks.

Similarly, the adaptive recombinant system 800 of FIG. 18 may operate ina fuzzy network environment. In FIG. 28, an adaptive recombinant system800C includes the adaptive system 100C of FIG. 27. Thus, the adaptiverecombinant system 800C may perform syndication and recombinationoperations, as described above, to generate structural subsets that arefuzzy networks.

The structural aspect 210 of adaptive system 100 may be comprised ofmultiple structures, comprising network-based structures,non-network-based structures, or combinations of network-basedstructures and non-network-based structures. In FIG. 29, the structuralaspect 210C includes multiple network-based structures andnon-network-based structures. The multiple structures of 210c may resideon the same computer system, or the structures may reside on separatecomputer systems.

FIG. 30 depicts a fuzzy network 520 syndicated to, and combined with, afuzzy network 530. Fuzzy network 520 contains objects 522 and 532. Fuzzynetwork 530 contains identical objects 522 and 532, which are depictedby shading.

The adaptive recombinant system 800 may determine objects, such as theobjects 522 and 532 of FIG. 30, to be identical through the objectevaluation function 830 (see FIG. 18). The object evaluation function830 may include a global or distributed management of unique identifiersfor each distinct object. These identifiers may be managed directly bythe adaptive recombinant system 800, or the adaptive recombinant systemmay rely on an auxiliary system, such as an operating system or anotherapplication, to manage object identification. Alternatively, theidentity relationship between objects may be determined thoughcomparisons of information associated with the object or through acomparison of the actual object content (information 232) itself.Associated content may be compared using text, graphic, video, or audiomatching techniques. A threshold may be set in determining identicalnessbetween two objects that is less than perfect matching to compensate forminor differences, versions, errors, or other non-substantivedifferences between the two objects, or to increase the speed of objectcomparisons by sacrificing some level of accuracy in identification ofidenticalness.

The combination of the fuzzy network 520 and the fuzzy network 530yields fuzzy network 540. In the fuzzy network 540, relationships thatwere unique in networks 520 and 530 are maintained. Where relationshipsor relationship indicators are common in fuzzy networks 520 and 530, theresolution function 834 (FIG. 20) is applied to create the relationshipand associated relationship indicators in the newly formed fuzzy network540.

For example, object 522 and object 532 are part of both fuzzy network520 and fuzzy network 530. A relationship 521 is depicted between object522 and object 532 in the fuzzy network 520, while a relationship 531 isdepicted between object 522 and object 532 in the fuzzy network 530.Where relationships 521 and 530 are of the same type, the resultingrelationship indicators 541 in the newly created fuzzy network 540 is anaverage of relationship indicators 521 and 531. That is, for determiningthe relationship between objects 522 and 532 in the fuzzy network 540,the resolution function 834 is a simple average function. In general,the resolution function 834 may be any mathematical function oralgorithm that takes as input two numbers between 0 and 1 inclusive, andoutputs a number between 0 and 1 inclusive.

The resolution function 834 may be derived from algorithms that applyappropriate usage behavior inferences. As a simple example, if therelationship value and associated indicator of one network has beenderived from the usage behaviors of highly informed or expert users,then this may have more weighting than the relationship value andassociated indicator of a second network for which the correspondingrelationship value was based on inferences associated with the usagebehaviors of a relatively sparse set of relatively uniformed users.

New relationships and associated relationship indicators that do notexist in originating fuzzy networks may also be generated by theadaptive recombinant system 800 upon fuzzy network creation. Theadaptive recommendations function 240 may be invoked directly to effectsuch relationship modifications, or it may be invoked in conjunctionwith fuzzy network maintenance functions.

For example, in FIG. 30, the fuzzy network 540 also contains a newrelationship and associated relationship indicators 542 that did notexplicitly exist in predecessor fuzzy networks 520 or 530. This is anexample of the invocation of the adaptive recommendations function 240being used by the adaptive recombinant system 800 in conjunction withthe fuzzy network operators 820, to automatically or semi-automaticallyadd a new relationship and associated relationship indicators to thenewly created fuzzy network.

The determination of a new relationship may be based on fuzzy networkstructural, usage, or content characteristics, and associatedinferencing algorithms. For example, in predecessor network 530, thetraffic patterns, combined with the organization of user referencedsubsets of 530, as one example, may support adding the relationship 542in the new network 540 that did not exist in the predecessor networks.The same procedure may be used to delete existing relationships (whichmay be alternatively viewed as just equivalent to setting a relationshipindicator to “0”), as desired. The algorithms for modifyingrelationships and relationship indicators, including adding and deletingrelationships, may incorporate global considerations with regard tooptimizing the overall topology of the fuzzy network by creatingeffective balance of relationships among objects to maximize overallusability of the network.

FIGS. 31A-31D illustrate the general approaches associated with fuzzynetwork syndication and combination by the adaptive recombinant system800, according to some embodiments. FIG. 31A illustrates a hypotheticalstarting condition, and depicts three individuals or organizations, 350,355, 360. It should be understood that where the term “organization” isused, it may imply a single individual or set of individuals that may ormay not be affiliated with any specific organization. A fuzzy network565 is used solely by, or resides within an organization 550. A fuzzynetwork 570 is used solely by, or resides within an organization 555. Anorganization 560 does not have a fuzzy network initially.

In FIG. 31B, a subset of the fuzzy network 565 is selected to form fuzzynetwork 565a. Fuzzy network 565a is then syndicated to the organization555, as fuzzy network 565b. The organization 555 then syndicates thefuzzy network 565b to the organization 560, as fuzzy network 565c. Fuzzynetwork 565a is a subset of fuzzy network 565, fuzzy network 565b issyndicated from fuzzy network 565a, and fuzzy network 565c is syndicatedfrom fuzzy network 565b. Thus, FIG. 31B illustrates how fuzzy networks,or subsets of networks, may be indefinitely syndicated among individualsor organizations by the adaptive recombinant system 800.

In FIG. 31C, the fuzzy network 565b in the organization 555, which wassyndicated from fuzzy network 565 (FIG. 31B), may be combined with thefuzzy network 570 already present in organization 555 (FIG. 31A), toform new fuzzy network 575. Fuzzy network 575 is then syndicated to theorganization 560 as fuzzy network 575a. Thus, FIG. 31C illustrates howfuzzy networks, or subsets of networks, may be combined to form newfuzzy networks.

In FIG. 31D, the organization 550 includes fuzzy network 565 (FIG. 31A)and fuzzy network 565a, a subset of fuzzy network 565 (FIG. 31B). Fuzzynetwork 575a, in the organization 560, is syndicated to the organization550, as fuzzy network 575b, such that organization 550 has three fuzzynetworks 565, 565a, and 575b. Fuzzy networks 565 and 575b may becombined, as shown, to form new fuzzy network 580 in the organization550.

The adaptive recombinant system 800 of FIG. 18 is capable of generatingsubsets, combining, and syndicating networks, as depicted in FIGS.31A-31D. The adaptive recombinant system may indefinitely enablesub-setting of fuzzy networks, syndicating them to one or moredestination fuzzy networks, and enabling the syndicated fuzzy networksto be combined with one or more fuzzy networks at the destinations. Ateach combination step, the resolution function 834, through applicationof the adaptive recommendations function 240 and network maintenancefunctions, may be invoked to create and update the structural aspect210, as appropriate.

The adaptive recombinant system 800 may efficiently support multipleadaptive systems 100, without reproducing the components used to supportsyndication and recombination for each adaptive system. FIG. 32, forexample, includes three adaptive systems 100P, 100Q, and 100R. Thesethree adaptive systems share the syndication function 810, the fuzzynetwork operators 820, and the object evaluation function 830. Inaddition, it should be remembered that multiple fuzzy networks may existinside an adaptive system 100, which may in turn form part of theadaptive recombinant system 800.

In addition to the resolution function 834, the adaptive recombinantsystem 800 may use the object evaluation function 830, to evaluate the“fitness” of the recombined fuzzy networks. The object evaluationfunction 830 may be completely automated, or it may incorporate explicithuman judgment. The networks that are evaluated to be most fit are thenrecombined among themselves, to create a new generation of fuzzynetworks.

The adaptive recombinant system 800 may also create random structuralchanges to enhance the diversity of the fuzzy networks in the nextgeneration. Or, the adaptive recombinant system 800 may use explicitnon-random-based rules to enhance the diversity of the fuzzy networks inthe next generation. Preferably, the inheritance characteristics fromgeneration to generation of adaptive recombinant fuzzy networks may bethat of acquired traits (Lamarckian). Or, the inheritancecharacteristics from generation to generation of adaptive recombinantfuzzy networks may be that of non-acquired, or random mutational, traits(Darwinian). For the Lamarckian embodiments, the acquired traits includeany structural adaptations that have occurred through system usage,syndications, and combinations.

Through application of these multi-generational approaches, fuzzynetworks are able to evolve against the selection criteria that areprovided. The fitness selection criteria may be determined throughinferences associated with fuzzy network usage behaviors, and may itselfco-evolve with the generations of adaptive fuzzy networks.

Fuzzy Content Network

In some embodiments, the structural aspect 210 of the adaptive system100 and of the adaptive recombinant system 800, as well as therespective structural subsets 280 generated by the adaptiverecommendations function 240, are networks of a particular form, a fuzzycontent network. A fuzzy content network 700 is depicted in FIG. 33.

The fuzzy content network 700, including content sub-networks 700a,700b, and 700c. The content network 700 includes “content,” “data,” or“information,” packaged in modules known as objects 710.

The content network 700 employs features commonly associated with“object-oriented” software to manage the objects 710. That is, thecontent network 700 discretizes information as “objects.” In contrast totypical procedural computer programming structures, objects are definedat a higher level of abstraction. This level of abstraction allows forpowerful, yet simple, software architectures.

One benefit to organizing information as objects is known asencapsulation. An object is encapsulated when only essential elements ofinteraction with other objects are revealed. Details about how theobject works internally may be hidden. In FIG. 34A, for example, theobject 710 includes meta-information 712 and information 714. The object710 thus encapsulates information 714.

Another benefit to organizing information as objects is known asinheritance. The encapsulation of FIG. 34A, for example, may formdiscrete object classes, with particular characteristics ascribed toeach object class. A newly defined object class may inherit some of thecharacteristics of a parent class. Both encapsulation and inheritanceenable a rich set of relationships between objects that may beeffectively managed as the number of individual objects and associatedobject classes grows.

In the content network 700, the objects 710 may be either topic objects710t or content objects 710c, as depicted in FIGS. 34B and 34C,respectively. Topic objects 710t are encapsulations that containmeta-information 712t and relationships to other objects (not shown),but do not contain an embedded pointer to reference associatedinformation. The topic object 710t thus essentially operates as a“label” to a class of information. The topic object 710 therefore justrefers to “itself” and the network of relationships it has with otherobjects 710.

Content objects 710c, as shown in FIG. 34C, are encapsulations thatcontain meta-information 36c and relationships to other objects 710 (notshown). Additionally, content objects 710c may include either anembedded pointer to information or the information 714 itself(hereinafter, “information 714”).

The referenced information 714 may include files, text, documents,articles, images, audio, video, multi-media, software applications andelectronic or magnetic media or signals. Where the content object 714csupplies a pointer to information, the pointer may be a memory address.Where the content network 700 encapsulates information on the Internet,the pointer may be a Uniform Resource Locator (URL).

The meta-information 712 supplies a summary or abstract of the object710. So, for example, the meta-information 712t for the topic object710t may include a high-level description of the topic being managed.Examples of meta-information 712t include a title, a sub-title, one ormore descriptions of the topic provided at different levels of detail,the publisher of the topic meta-information, the date the topic object710t was created, and subjective attributes such as the quality, andattributes based on user feedback associated with the referencedinformation. Meta-information may also include a pointer to referencedinformation, such as a uniform resource locator (URL), in oneembodiment.

The meta-information 712c for the content object 710c may includerelevant keywords associated with the information 714, a summary of theinformation 714, and so on. The meta-information 712c may supply a“first look” at the objects 710c. The meta-information 712c may includea title, a sub-title, a description of the information 714, the authorof the information 714, the publisher of the information 714, thepublisher of the meta-information 712c, and the date the content object710c was created, as examples. As with the topic object 710t,meta-information for the content object 710c may also include a pointer.

In FIG. 33, the content sub-network 700a is expanded, such that bothcontent objects 710c and topic objects 710t are visible. The variousobjects 34 of the content network 700 are interrelated by degrees, usingrelationships 716 (unidirectional and bidirectional arrows) andrelationship indicators 716 (values). (The relationships 716 andrelationship indicators 718 are similar to the relationships andrelationship indicators depicted in FIG. 13A, above, as well as otherfigures included herein.) Each object 710 may be related to any otherobject 710, and may be related by a relationship indicator 718, asshown. Thus, while information 714 is encapsulated in the objects 710,the information 714 is also interrelated to other information 714 by adegree manifested by the relationship indicators 718.

The relationship indicator 718 is a numerical indicator of therelationship between objects 710. Thus, for example, the relationshipindicator 718 may be normalized to between 0 and 1, inclusive, where 0indicates no relationship, and 1 indicates a subset relationship. Or,the relationship indicators 718 may be expressed using subjectivedescriptors that depict the “quality” of the relationship. For example,subjective descriptors “high,” “medium,” and “low” may indicate arelationship between two objects 710.

The relationship 716 between objects 710 may be bidirectional, asindicated by the double-pointing arrows. Each double-pointing arrowincludes two relationship indicators 718, one for each “direction” ofthe relationships between the objects 710.

As FIG. 33 indicates, the relationships 716 between any two objects 710need not be symmetrical. That is, topic object 710t 1 has a relationshipof “0.3” with content object 710c2, while content object 710c2 has arelationship of “0.5” with topic object 710t1. Furthermore, therelationships 716 need not be bi-directional—they may be in onedirection only. This could be designated by a directed arrow, or bysimply setting one relationship indicator 718 of a bi-directional arrowto “0,” the null relationship value.

The content networks 700A, 700B, 700C may be related to one anotherusing relationships of multiple types and associated relationshipindicators 718. For example, in FIG. 33, content sub-network 700a isrelated to content sub-network 700b and content sub-network 700c, usingrelationships of multiple types and associated relationship indicators718. Likewise, content sub-network 700b is related to contentsub-network 700a and content sub-network 700c using relationships ofmultiple types and associated relationship indicators 718.

Individual content and topic objects 710 within a selected contentsub-network 700a may be related to individual content and topic objects710 in another content sub-network 700b. Further, multiple sets ofrelationships of multiple types and associated relationship indicators718 may be defined between two objects 710

For example, a first set of relationships 716 and associatedrelationship indicators 718 may be used for a first purpose or beavailable to a first set of users while a second set of relationships716 and associated relationship indicators 718 may be used for a secondpurpose or available to a second set of users. For example, in FIG. 33,topic object 710t1 is bi-directionally related to topic object 710t2,not once, but twice, as indicated by the two double arrows. Anindefinite number of relationships 716 and associated relationshipindicators 718 may therefore exist between any two objects 710 in thefuzzy content network 700. The multiple relationships 716 may correspondto distinct relationship types. For example, a relationship type mightbe the degree an object 710 supports the thesis of a second object 710,while another relationship type might be the degree an object 710disconfirms the thesis of a second object 710. The content network 700may thus be customized for various purposes and accessible to differentuser groups in distinct ways simultaneously.

The relationships among objects 710 in the content network 700, as wellas the relationships between content networks 700a and 700b, may bemodeled after fuzzy set theory. Each object 710, for example, may beconsidered a fuzzy set with respect to all other objects 710, which arealso considered fuzzy sets. The relationships among objects 710 are thedegrees to which each object 710 belongs to the fuzzy set represented byany other object 710. Although not essential, every object 710 in thecontent network 700 may conceivably have a relationship with every otherobject 710.

The topic objects 710t encompass, and are labels for, very broad fuzzysets of the content network 700. The topic objects 710t thus may belabels for the fuzzy set, and the fuzzy set may include relationships toother topic objects 710t as well as related content objects 710c.Content objects 710c, in contrast, typically refer to a narrower domainof information in the content network 700.

The adaptive system 100 of FIG. 1 may operate in a fuzzy content networkenvironment, such as the one depicted in FIG. 33. In FIG. 35, anadaptive system 100D includes a structural aspect 210D that is a fuzzycontent network. Thus, adaptive recommendations 250 generated by theadaptive system 100D are also structural subsets that are themselvesfuzzy content networks.

Similarly, the adaptive recombinant system 800 of FIG. 18 may operate ina fuzzy content network environment. In FIG. 36, an adaptive recombinantsystem 800D includes the adaptive system 100D of FIG. 35. Thus, theadaptive recombinant system 800D may perform syndication andrecombination operations, as described above, to generate structuralsubsets that are fuzzy content networks.

Extended Fuzzy Structures in Fuzzy Networks

The fuzzy network model may be extended to the organizational structureof the meta-information and other affiliated information associated witheach network node or object. In a fractional degree of separation systemstructure, depicted in FIG. 37, meta-information and affiliatedinformation may be structured in distinct tiers or rings around theinformation, with each tier designated as a fractional degree ofseparation 750. The chosen parameters for the degrees of separation ofthe meta-information will depend on the definition of the calculation ofthe degrees of separation between any two nodes, specifically dependingon the choice of the scaling factor on in the formula. This extendedfuzzy network structure may be utilized to implement a fuzzy contentnetwork system structure, or any other fuzzy network-based structure.

Meta-information 754 associated with information or interactiveapplications 752 may include, but is not limited to, descriptiveinformation about the object such as title, publishing organization,date published, physical location of a physical object, an associatedphoto or picture, summary or abstracts, a plurality of reviews, etc.Meta-information 754 may also include dynamic information such as expertand community ratings of the information, feedback from users, and moregenerally, any relevant set of, or history of, usage behaviors describedin Table 1. The meta-information 754 may also include information aboutrelationships to other nodes in the network. For example, themeta-information 754 may include the relationships with other nodes inthe networks, including an identification code for each related node,the types of relationships, the direction of the relationships, and thedegree of relatedness of each relationship.

The meta-information 754 may be defined within tiers of fractionaldegree of separation between zero and one. For example, the most tightlybound meta-information might be in a tier at degree of separation of 0.1and less tightly bound meta-information might be in a tier at degree ofseparation of 0.8.

Where the degrees of separation calculated between any two nodes in thefuzzy network are between 0 and 1, the meta-information tiers would moreappropriately be designated with negative (possibly fractional) degreesof separation. For example, the most tightly bound meta-information 752may be in a tier at degree of separation of −5 and less tightly boundmeta-information may be in a tier at degree of separation of −1.

The meta-information tiers may distinguish between staticmeta-information such as the original author of the associatedinformation, and dynamic information such as the total number ofaccesses of the associated information through a computer system.

The fractional degree of separations of less than one may correspond tocompound objects 756. For example, a picture object plus a textbiography object may constitute a person object. For typical fuzzycontent network operations the compound object would generally behave asif it was one object.

The fractional degree of separations of less than one may correspond toa list of objects with which the present object has a specificsequential relation 758. For example, this may include workflowsequences in processes. These sequential relationships imply a tighter“binding” between objects than the relationships associated with otherobjects in the fuzzy network 770, hence a smaller fractional degree ofseparation is employed for sequential relationships.

All meta-information may explicitly be content objects that relate toassociated information by a fractional degree of separation of less thanone, and may relate to other content objects in the network by afractional degree of separation that may be greater than or equal toone. This can be described by a degree-of-separation matrix. Everyobject is arrayed in sequence along both the matrix columns and thematrix rows. Each cell of the matrix corresponds to the degree ofseparation between the two associated objects. The cells in the maindiagonal of the degree of separation matrix are all zeroes, indicatingthe degree of separation between an object and itself is zero. All othercells will contain a non-zero number, indicating the degree ofseparation between the associated objects, or a designator indicatingthat the degree of separation is essentially infinite in the case whenthere is no linked path at all between the associated objects.

Personalized Fuzzy Content Network Subsets

Recall that users 200 of the adaptive system 100 of FIG. 1 may tag orstore subsets of the structural aspect 210 for personal use, or to sharewith others. Likewise, users 200 of the adaptive recombinant system 800may tag subsets of the fuzzy content network, whether for personal useor to share with others.

FIG. 38 is a screenshot 770 generated by the Epiture software system. A“My World” icon 772 invites the viewer to “create your own knowledgenetwork” by clicking on the icon. The icon 772 further states, “Makeyour own topics and store relevant resources in them.” The term “store”in the icon 772 may simply imply tagging information—no referencedinformation need necessarily be physically copied and stored, althoughphysical copying and storing may be implemented.

Users of the Epiture software system may select content objects and tagthem for storage in their personal fuzzy network. Optionally, relatedmeta-information and links to other objects in the original fuzzynetwork may be stored with the content object. Users may also storeentire topics in their “My World” personal fuzzy network. Furthermore,users may use fuzzy network operators to create synthetic topics. Forexample, a user might apply an intersection operator to Topic A andTopic B, to yield Topic C. Topic C could then be stored in the personalfuzzy network. Union, difference and other fuzzy network operators mayalso be used in creating new fuzzy network subsets to be stored in aprivate fuzzy content network.

Users of the Epiture software system may directly edit their personalfuzzy networks, including the names or labels associated with contentobjects and topic objects, as well as other meta-information associatedwith content and topic objects. The screenshot 770 of FIG. 38 features a“personal topics” icon, allowing the user to explicitly edit thenetwork, thus generating an explicitly requested structural subset 280.Users may also create new links among content and topics in theirpersonal fuzzy network, alter the degree of relationship of existinglinks, or delete existing links altogether, to name a few features ofthe Epiture software system.

Users may selectively share their personal fuzzy networks by allowingother users to have access to their personal networks. Convenientsecurity options may be provided to facilitate this feature.

Usage Behavior Information

Users of the Epiture software system may have the ability to reviewpersonal, sub-community or community usage behaviors over time. This mayinclude trends related to popularity, connectedness, influence or anyother relevant usage metric. FIG. 39 is a screenshot 780 showing trendinformation display functionality associated with the Epiture softwaresystem.

Navigational histories, such as access paths, may be available forreview, with capabilities for making queries against the historiesthough application of selection criteria. FIG. 40 depicts a screenshot790. The screenshot 790 is an example of navigational usage behaviorinformation display and query functions associated with the “MyPaths”function of the Epiture software system. With appropriate authorizationsand permissions, users may be able to access any other usage behaviors,such as online information accesses, traffic patterns and click streamsassociated with navigating the system structure, including buying andselling behaviors; physical locational cues associated with stationaryor mobile use of the system; collaborative behaviors among system usersthat include written and oral communications, and among and with groupsof system users (communities) or system users and people outside of thesystem; referencing behaviors of system users—for example, the taggingof information for future reference; subscription and otherself-profiling behavior of users and associated attributes e.g.,subscribing to updates associated with particular aspects of the systemor explicitly identifying interests or affiliations, such as jobfunction, profession, organization, etc, and preferences such asrepresentative skill level (for example, novice, business user, advancedetc), preferred method of information receipt or learning style such asvisual or audio; and relative interest levels in other communities anddirect feedback behaviors, such as the ratings or direct writtenfeedback associated with objects or their attributes such as theobjects' author, publisher, etc.

Users may also have access to system usage information that may becaptured and organized to retain temporal information associated withusage behaviors, including the duration of behaviors and the timing ofthe behaviors, where the behaviors may include those associated withreading or writing of written or graphical material, oralcommunications, including listening and talking, or duration of physicallocation of a system user, potentially segmented by user communities oraffinity groups may be available for review by users.

The above usage behaviors may be available to users in raw form, or insummarized form, potentially after application of statistical or othermathematical functions are applied to facilitate interpretation. Thisinformation may be presented in a graphical format.

Adaptive Recommendations in Fuzzy Content Networks

Adaptive recommendations or suggestions may enable users to moreeffectively navigate through the fuzzy content network. As with othernetwork embodiments described herein, the adaptive recommendationsgenerated from a fuzzy content network may be in the context of acurrently accessed content object or historical path of accessed contentobjects during a specific user session, or the adaptive recommendationsmay be without context of a currently accessed content object or currentsession path.

In the most generalized approach, adaptive recommendations in a fuzzycontent network combine inferences from user community behaviors andpreferences, inferences of sub-community or expert behaviors andpreferences, and inferences of personal user behaviors and preferences.Usage behaviors that may be used to make preference inferences include,but are not limited to, those that are described in Table 1. Theseusage-based inferences may be augmented by automated inferences aboutthe content within individual and sets of content objects usingstatistical pattern matching of words or phrases within the content.Such statistical pattern matching may include, but not limited to,Bayesian analysis, neural network-based methods, k-nearest neighbor,support vector machine-based techniques, or other statistical analyticaltechniques.

Community Preference Inferences

Where the structural aspect 210 of the adaptive system 100 or theadaptive recombinant system 800 is a fuzzy content network, usercommunity preferences may be inferred from the popularity of individualcontent objects and the influence of topic or content objects, aspopularity and influence were defined above. The duration of access orinteraction with topic or content objects by the user community may beused to infer preferences of the community.

Users may subscribe to selected topics, for the purposes of e-mailupdates on these topics. The relative frequency of topics subscribed toby the user community as a whole, or by selected sub-communities, may beused to infer community or sub-community preferences. Users may alsocreate their own personalized fuzzy content networks through selectionand saving of content objects and/or topics objects. The relativefrequency of content objects and/or topic objects being saved inpersonal fuzzy content networks by the user community as a whole, or byselected sub-communities, may be used to also infer community andsub-community preferences. These inferences may be derived directly fromsaved content objects and/or topics, but also from affinities the savedcontent and/or topic objects have with other content objects or topicobjects. Users can directly rate content objects when they are accessed,and in such embodiments, community and sub-community preferences mayalso be inferred through these ratings of individual content objects.

The ratings may apply against both the information referenced by thecontent object, as well as meta-information such as an expert review ofthe information referenced by the content object. Users may have theability to suggest content objects to other individuals and preferencesmay be inferred from these human-based suggestions. The inferences maybe derived from correlating these human-based suggestions with inferredinterests of the receivers if the receivers of the human-basedsuggestions are users of the fuzzy content object system and have apersonal history of content objects viewed and/or a personal fuzzycontent network that they may have created.

The physical location and duration of remaining in a location of thecommunity of users, as determined by, for example, a global positioningsystem or any other positionally aware system or device associated withusers or sets of users, may be used to infer preferences of the overalluser community.

Sub-Community and Expert Preference Inferences

Community subsets, such as experts, may also be designated. Expertopinions on the relationship between content objects may be encoded asaffinities between content objects. Expert views may be directlyinferred from these affinities. An expert or set of experts may directlyrate individual content items and expert preferences may be directlyinferred from these ratings.

The history of access of objects or associated meta-information bysub-communities, such as experts, may be used to infer preferences ofthe associated sub-community. The duration of access or interaction withobjects by sub-communities may be used to infer preferences of theassociated sub-community.

Experts or other user sub-communities may have the ability to createtheir own personalized fuzzy content networks through selection andsaving of content objects. The relative frequency of content objectssaved in personal fuzzy content networks by experts or communities ofexperts may be used to also infer expert preferences. These inferencesmay be derived directly from saved content objects, but also fromaffinities the saved content objects have with other content objects ortopic objects.

The physical location and duration of remaining in a location ofsub-community users, as determined by, for example, a global positioningsystem or any other positionally aware system or device associated withusers or sets of users, may be used to infer preferences of the usersub-community.

Personal Preference Inferences

Users may subscribe to selected topics, for the purposes of, forexample, e-mail updates on these topics. The topic objects subscribed toby the user may be used to infer personal preferences. Users may alsocreate their own personalized fuzzy content networks through selectionand saving of content objects. The relative frequency of content objectssaved in personal fuzzy content networks by the user may be used toinfer the individual's personal preferences. These inferences may bederived directly from saved content objects, but also from affinitiesthe saved content objects have with other content objects or topicobjects. Users may directly rate content objects when they are accessed,and in such embodiments, personal preferences may also be inferredthrough these ratings of individual content objects.

The ratings may apply against both the information referenced by thecontent object, as well as any of the associated meta-information, suchas an expert review of the information referenced by the content object.A personal history of paths of content objects viewed may be stored.This personal history may be used to infer user preferences, as well astuning adaptive recommendations and suggestions by avoiding recommendingor suggesting content objects that have already been recently viewed bythe individual. The duration of access or interaction with topic orcontent objects by the user may be used to infer preferences of theuser.

The physical location and duration of remaining in a location of theuser as determined by, for example, a global positioning system or anyother positionally aware system or device associated with the user, maybe used to infer preferences of the user.

Adaptive Recommendations and Suggestions

Adaptive recommendations in fuzzy content networks combine inferencesfrom user community behaviors and preferences, inferences ofsub-community or expert behaviors and preferences, and inferences ofpersonal user behaviors and preferences as discussed above, to presentto a fuzzy network user or set of users one or more fuzzy networksubsets (one or more objects and associated relationships) that usersmay find particularly interesting given the user's current navigationalcontext. These sources of information, all of which are external to thereferenced information within specific content objects, may be augmentedby search algorithms that use text matching or statistical patternmatching or learning algorithms to provide information on the likelythemes of the information embedded or pointed to by individual contentobjects.

The navigational context for a recommendation may be at any stage ofnavigation of a fuzzy network (e.g., during viewing a particular contentobject) or may be at a time when the recommendation recipient is notengaged in directly navigating the fuzzy network. In fact, therecommendation recipient need never have explicitly used the fuzzynetwork associated with the recommendation. As an example, FIG. 41depicts in-context, displayed adaptive recommendations associated withthe Epiture system.

Some inferences will be weighted as more important than other inferencesin generating a recommendation, and theses weightings may vary overtime, and across recommendation recipients, whether individualrecipients or sub-community recipients. For example, characteristics ofcontent and topics explicitly stored by a user in a personal fuzzynetwork would typically be a particularly strong indication ofpreference as storing network subsets requires explicit action by auser. In most recommendation algorithms, this information will thereforebe more influential in driving adaptive recommendations than, say,general community traffic patterns in the fuzzy network.

The recommendation algorithm may particularly try to avoid recommendingto a user content that the user is already familiar with. For example,if the user has already stored a content object in a personal fuzzynetwork, then the content object might be a very low ranking candidatefor recommending to the user. Likewise, if the user has recently alreadyviewed the associated content object (regardless of whether it was savedto his personal fuzzy network), then the content object would typicallyrank low for inclusion in a set of recommended content objects. This maybe further tuned through inferences with regard to the duration that anassociated content object was viewed (for example, it may be inferredthat a lengthy viewing of a content object is indicative of increasedlevels of familiarity.

The algorithms for integrating the inferences may be tuned or adjustedby the individual user. The tuning may occur as adaptive recommendationsare provided to the user, by allowing the user to explicitly rate theadaptive recommendations. The user may also set explicit recommendationtuning controls to tune the adaptive recommendations to her particularpreferences. For example, a user might guide the recommendation functionto place more relative weight on inferences of expert or other usercommunities' preferences versus inferences of the user's own personalpreferences. This might be particularly true if the user was relativelyinexperienced in the particular domain of knowledge. As the user'sexperience grew, he might adjust the weighting toward inferences of theuser's personal preferences versus inferences of expert preferences.

Fuzzy network usage metrics described above such as popularity,connectedness, and influence may be employed by the recommendationalgorithm as convenient summaries of community, sub-community andindividual user behavior with regard to the fuzzy network. These metricsmay be used individually or collectively by the recommendation algorithmin determining the recommended network subset or subsets to present tothe recommendation recipient.

Adaptive recommendations which are fuzzy network subsets may bedisplayed in variety of ways to the user. They may be displayed as alist of content objects (where the list may be null or a single contentobject), they may include content topic objects, and they may display avarying degree of meta-information associated with the content objectsand/or topic objects. Adaptive recommendations may be delivered througha web browser interface, through e-mail, through instant messaging,through XML-based feeds, RSS, or any other approach in which the uservisually or acoustically interprets the adaptive recommendations. Therecommended fuzzy network subset may be displayed graphically. Thegraphical display may provide enhanced information that may includedepicting linkages among objects, including the degree of relationship,among the objects of the recommended fuzzy network subset, and mayoptionally indicate through such means of size of displayed object orcolor of displayed object, designate usage characteristics such aspopularity of influence associated with content objects and topicobjects in the recommended network subset. Adaptive recommendations maybe delivered for interpretation of users by other than visual senses;for example, the recommendation may be delivered acoustically, typicallythrough oral messaging.

The recommended structural subsets 280, combinations of topic objects,content objects, and associated relationships, may constitute most oreven all of the user interface, which may be presented to a system useron a periodic or continuous basis. Such embodiments correspond toembodiment variations of 2130, 2140, 2150 and 2160 of the framework 2000in FIG. 42, below.

In addition to the recommended fuzzy network subset, the recommendationrecipient may be able to access information to help gain anunderstanding from the system why the particular fuzzy network subsetwas selected as the recommendation to be presented to the user. Thereasoning may be fully presented to the recommendation recipient asdesired by the recommendation recipient, or it may be presented througha series of interactive queries and associated answers, as arecommendation recipient desires more detail. The reasoning may bepresented through display of the logic of the recommendation algorithm.A natural language (such as English) interface may be employed to enablethe reasoning displayed to the user to be as explanatory and human-likeas possible.

In addition to adaptive recommendations of fuzzy network subsets,adaptive recommendations of some set of users of the fuzzy network maybe determined and displayed to recommendation recipients, typicallyassuming either implicit or explicit permission is granted by such usersthat might be recommended to other users. The recommendation algorithmmay match preferences of other users of the fuzzy network with thecurrent user. The preference matches may include the characteristics offuzzy network subsets stored by users or other fuzzy networkreferencing, their topic subscriptions and self-profiling, theircollaborative patterns, their direct feedback patterns, their physicallocation patterns, their fuzzy network navigational and access patterns,and related temporal cues associated with these usage patterns.Information about the recommended set of users may be displayed to auser. This information may include names, as well as other relevantinformation such as affiliated organizations and contact information. Itmay also include fuzzy network usage behavioral information, such as,for example, common topics subscribed to, common physical locations,etc. As in the case of fuzzy network subset adaptive recommendations,the adaptive recommendations of other users may be tuned by anindividual user through interactive feedback with the system.

Adaptability/Extensibility Framework

FIG. 42 depicts an adaptability/extensibility framework 2000 used todistinguish the adaptive system 100 and the adaptive recombinant system800 from the prior art, described herein as an “identified system.” Theframework 2000 is a two-dimensional representation comprising a verticaldimension 2002 and a horizontal dimension 2004, each dimension havingfour categories. The vertical dimension 2002 of the framework 2000indicates the “degree of adaptiveness” of the identified system. The“degree of adaptiveness” is the degree to which the identified system isadaptive to individual users or to communities of users of the system.

The vertical dimension 2002 includes four categories across a range, thefirst category being least adaptive and the fourth category being themost adaptive. The categories are: non-adaptive (does not dynamicallycustomize); displays adaptive recommendations 250 (where “displays”includes not only visual delivery of adaptive recommendations, butdelivery in other modes, such as audio); provides adaptiverecommendations 250 that update structure or content (where thestructure and/or content of the system are dynamically updated); andprovides a continuous, fully adaptive interface. The adaptive system 100and the adaptive recombinant system 800 are capable of all degrees ofadaptiveness depicted in the framework 2000, including providing acontinuous, fully adaptive interface.

The horizontal dimension 2004 of the framework 2000 represents thedegree of extensibility of the identified system. The “degree ofextensibility” or “degree of portability” denotes the ability to“syndicate” the system 100 or subsets of the system 100, as well as theability to create combinations of systems. Syndication, as used herein,describes ability to share systems or portions of systems, which mayinclude actual transfer of the system structural and content aspectsacross computer and communications network hardware, or may describe thevirtual transfer of a system on a particular set of computer hardware.Recall that a structural subset 280 is a portion of the structuralaspect 210 of a system, including one or more objects 212 and theirassociated relationships 214, which may be replicated (see FIG. 4).Structural subsets may be syndicated by the adaptive recombinant system800.

The horizontal dimension 2004 includes four categories across a range,the first category being least extensible and the fourth category beingthe most extensible. The categories are: no syndication (the identifiedsystem has no ability to share content); individual content syndication(individual items of content within the identified system can beshared); structural subset syndication (structural subsets of theidentified system can be shared); and recombinant structures syndication(structural subsets of the identified system can be shared and combinedto create new systems). The adaptive recombinant system 800 is capableof all degrees of extensibility depicted in the framework 2000,including the most portable feature, recombinant structures syndication.

The framework 2000 is divided into sixteen numbered blocks, arrangedaccording to their relationship to the horizontal dimension 2002 (degreeof adaptiveness) and the vertical dimension 2004 (degree ofextensibility). The majority of prior art systems are confined to thelower left portion of the framework 2000. For example, most prior artsystem are non-adaptive and include no syndication capabilities (block2010). Current computer operating systems (e.g. Microsoft XP™), businessproductivity applications (e.g., Microsoft Office™), enterpriseapplications (e.g., SAP), and search utilities (e.g., Google®) areassociated with block 2010 of the framework 2000.

Some prior art systems syndicate items of content or sets of contentfiles. These may be based on a central syndication clearinghouse (e.g.,Napster), or may be more purely peer-to-peer in operation (e.g.,Gnutella). Such systems are associated with block 2020 of the framework2000.

Other prior art systems provide users with merchandise recommendationsbased on their buying habits, as well as the buying habits of customerswho have purchased common merchandise (e.g., Amazon.com®). However,these systems do not truly deliver adaptive recommendations as definedherein, whether by displaying adaptive recommendations 250 (block 2050),updating structure or content (block 2090) or providing a continuous,fully adaptive interface (block 2130). This is because, among otherreasons, the scope of the usage behaviors tracked by such prior artsystems is limited to purchasing and associated behaviors.

In contrast, for the adaptive system 100 and the adaptive recombinantsystem 800, more generalized system usage behaviors 247 are tracked andused to deliver adaptive recommendations 250 to the user 200 and to theadaptive (recombinant) system itself. Thus, prior art systems such asAmazon.com are deemed non-adaptive (block 2010) in the framework 2000.Blocks 2010 and 2020 of the framework 2000 thus represent the extent ofprior art system capabilities with regard to system adaptation (verticaldimension 2002) and portability (horizontal dimension 2004).

In contrast, the adaptive recombinant system 800 includes theadaptability and portability associated with the remaining blocks of theframework 2000. For example, the adaptive recombinant system 800 iscapable of syndicating non-adaptive structural subsets 280 of the system800 (block 2030), as well as syndicating non-adaptive recombinantstructures (block 2040). Thus, the adaptive recombinant system 800exhibits a high degree of extensibility, fully covering the horizontaldimension 2004 of the framework 2000.

The vertical dimension 2002 is likewise embodied both by the adaptivesystem 100 and the adaptive recombinant system 800. While the adaptivesystem 100 displays adaptive recommendations 250 where no syndicationoccurs (block 2050), the adaptive recombinant system 800 furtherdisplays adaptive recommendations 250 where individual content issyndicated (block 2060), where structural subsets 280 are syndicated(block 2070) and where recombinant structures are syndicated (block2080).

Moving up the vertical dimension 2002, the adaptive system 100 providesadaptive recommendations 250 that update the structural aspect 210and/or the content aspect 230 of the system where there is nosyndication (block 2090), and the adaptive recombinant system 800provides adaptive recommendations that update the structural or contentaspects where individual content is syndicated (block 2100), wherestructural subsets 280 are syndicated (block 2110), and whererecombinant structures are syndicated (block 2120).

Finally, the adaptive recombinant system 800 provides a continuous,fully adaptive interface for all four categories of syndication (blocks2130, 2140, 2150, and 2160) while the adaptive system 100 does so wherethere is no syndication (block 2130). Thus, the adaptive system 100 andthe adaptive recombinant system 800 provide various degrees ofadaptiveness and extensibility, as represented in the framework 2000.

Sample Recommendations Function and Algorithm

In this example, two types of adaptive recommendations are delivered tothe user. The adaptive recommendations are calculated by a set ofalgorithms based on the systems objects being currently navigated, therelationships of the currently accessed object, the user's navigationpath, profile preferences, community membership and level of relevancedepending on context and the user's personal library of referencedobjects. Recall that a “user” may refer to not only humans, but toanother system or adaptive network. In other words, two or more adaptivesystems may be “users” of each other.

Two types of adaptive recommendations based on a fuzzy content networkstructure are described in Table 4. One skilled in the art may applyother variations of adaptive recommendations and associated algorithms.

TABLE 4 Two Recommendations Algorithms Type Delivery characteristicsin-context when user is accessing or may be delivered in real-(suggestions) interacting, accessing, or time updating content objectavailable in display pages for retrieval/editing may be optimized forresponsiveness and “fast” learning of user preferences out-of-context noexplicit access of inferences may be (recommendations) content object byuser updated in real time or periodically available in display pages forretrieval may be optimized for accuracy and understand- ing of userpreferencesThe first adaptive recommendations type, in-context recommendations, orsuggestions, are delivered to the user when the user is interacting,accessing, or updating a content object. In-context recommendations maybe delivered in real time, may be displayed for retrieval and editing,and may be optimized for responsiveness and the “fast” learning of theuser's preferences.

The second adaptive recommendations type, out-of-contextrecommendations, is a “push” recommendation approach. Based oninferences about the user's preferences, the network is aligned to adaptto the preferences. The out-of-context recommendations thus “surprise”the user with recommendations of relevant objects of interest withoutspecific explicit context from the user. Relevant characteristics forout-of-context recommendations include the real-time or periodicupdating of inferences and the ability to provide adaptiverecommendations in display pages or via other modes of communication forretrieval Further, the out-of-context recommendations algorithm may beoptimized for accuracy and understanding of user preferences

Adaptive Recommendations Function Example

FIG. 43 is a flow diagram depicting the operation of an adaptiverecommendations function 900 used in the Epiture software system,according to some embodiments. The Epiture software system is oneimplementation of an adaptive recombinant system, such as the system 800depicted in FIG. 18. The network described in this example is a fuzzycontent network. Recall that the adaptive recommendations functionincludes algorithms for generating adaptive recommendations to a user inthe form of structural subsets 280.

The following data is used by the adaptive recommendations function 900in generating recommendations:

-   -   1) the communities that a user is a member of    -   2) relationships between those communities and user's        preferences (including temporal dimensions that may indicate        strengthened or weakened interest in those communities)    -   3) a user's or other pre-defined system explicit preference of        those communities in this context (e,g, business rules for a        process, novice vs advanced users),    -   4) the user's personal topics (where objects of high relevance        have been ‘saved’ for future explorations) and the relationships        between those topics    -   5) the content in those topics and their interrelations, or        personal highest recommendation objects

The adaptive recommendations function 900 begins by determining personalhighest recommendation areas, or PHRAs of the user (block 902). PHRAsare, generated by determining the highest relevance sums ofco-topic-community relationships. To illustrate this step, Table 5includes an abbreviated matrix of topics and communities on one axisversus content objects and topic objects on the other matrix, withnumerical relationships between the two axes.

TABLE 5 Relationships between objects in fuzzy content network topic Atopic B topic C community X object 1 (article) 5 3 2 0 object 2(presentation) 1 4 — 5 object 3 (book) 3 3 5 2 topic A — 3 — 5 total 912  7 12  In this limited example, there are three topics, topic A,topic B, and topic C, and one community, community X, that have varyingdegrees of relationship (rated between 1 and 5) to other objects in thesystem: object 1 (an article), object 2 (a presentation), object 3 (abook), and topic A. Calculating the highest sum of relationships for theparticular context (total row) results in the generation of PHRAs.

In Table 5, topic B and community X have the highest relationship sumsthus two PHRAs are found in this example. This method will oftengenerate many PHRAs, which sometimes may be too many to make usefulsuggestions from. For example, there may be a dozen or more PHRAs withthe same value. In this case, the tie breakers are the data that informson relationships between topics and communities.

For example, in Table 5, topic A has a strong relationship (5) tocommunity X. Topic A itself has a high total score. Thus, the adaptiverecommendations function 900 assigns a dynamic weighting to topic A'srelevance to community X, to strengthen community X's result. In thiscase, if it was desirable to have only one PHPA, community X would bechosen. In some embodiments, the top 3-5 PHRAs are selected by theadaptive recommendations function 900.

Building on this procedure, the storing of the dynamic weightingsgenerated in this process can be useful as an additional recommendationmechanism. This approach allows the adaptive recommendations function900, at the end of processing, to compare which recommendation isactually selected by the user from the top suggestions generated. Ifthere is a discrepancy or convergence, the weightings may be examinedand used as a way to strengthen or weaken the relationships betweentopics, objects and communities for this user's particular context.

The adaptive recommendations function 900 also determines Epiture'shighest recommendation area, or EHRA (block 904). Recall that, in theadaptive recombinant system 800, relationships between objects, topicsand communities, may be made by experts. There may also be explicitbusiness rules in the system to conform) to, for example in the form ofa business process. II addition, the relationship context may bedelivered from another fuzzy content network or instance of the adaptiverecombinant system, in particular when ‘training’ a new knowledgenetwork or integrating existing networks. The Epiture software systemincludes these features in determining EHRAs.

A set of Epiture's highest recommendation areas (EHRA) may be generatedby selecting related topics or communities with higher relevance valuesto the current object. The EHRAs are weighted appropriately to thesituation, either by system rules, or by user preferences.

The adaptive recommendations function 900 also determines Epiture'shighest recommended objects (block 906). Again, this step usesrelationships already in existence in the system, either an averageacross all relationships and quality ratings, or tuned to select aparticular set of relationship types or quality ratings. From thesedata, a set of Epitures highest recommendation objects (EHRO) may begenerated by selecting related content objects with higher relevancevalues (with relevance defined by context of both the object in questionand system ‘priorities’) to the current object.

Although steps 902, 904, and 906 are presented in a particular order inFIG. 43, they may be implemented by the adaptive recommendationsfunction 900 in a different order than the one shown. The adaptiverecommendations function 900 next combines the PHRA, EHRA and EHRO datato determine what will be recommended to the user (block 908).Initially, if a set of objects score highly in both PHRA and EHRA, thenthey will be the objects recommended first. Depending on the amount ofrecommendation results that are prespecified by the adaptiverecommendations function, this initial set of recommended objects may besufficient.

If not, however, the adaptive recommendations function 900 determineswhether it can find any objects in EHRO that also exist in the PHRA. Ifso, those results will be returned and the operation ends even thoughthe selected objects are a second tier of the recommended objects. Toensure that the user realizes this, a relevance weighting may beassigned, and graphically indicated if needed.

A third tier of recommended objects may be found by finding any objectsin the EHRO that exist in the EHPA, using quality, relationships typesand values and other attributes as guides for making the selection.

If a sufficient set of recommendation objects have been found (the “yes”prong of block 910), the adaptive recommendations function 900 removesduplicated objects in the potential recommendations determined thus far(block 908). This step is particularly relevant where the users of theEpiture software system are human users who have been browsing thesystem for some time period. Such users generally do not wish to berecommended content they have already read, visited, or used. If theuser has already visited some of the selected recommended objects withina predetermined time period, say, in the last 24 hours, or, if some ofrecommended objects are already in the user's personal topic library,the adaptive recommendations function 900 determines the object to beunnecessary to recommend. Thus, such objects are removed from therecommendation object set.

Where objects removed in this manner cause the available adaptiverecommendations to be insufficient or empty (the “no” prong of block914), or where enough adaptive recommendations were not producedinitially (the “no” prong of block 910), the adaptive recommendationsfunction 900 proceeds to determine the most popular jump objects in thepath of a community (block 916).

The adaptive recommendations function 900 examines the paths of otherusers who have browsed the object. Given criteria such as similarcommunity membership to the current user, content quality rating anddistribution, overall popularity, and other attributes, it is determinedwhich objects to recommend based on prior usage. This fourth tier ofrecommendation objects (besides PHRAs, EHRAs, and EHROs) is designatedas a second set of Epiture's highest recommended objects or EHRO2.

This step (block 916) may be helpful in the case of integrating two ormore networks together. Since the relationship context and attributes ofthe objects in the network may be ‘carried’ over or ported into th e newnetwork, the objects may ‘look’ for their prior relationships andsegment based on usage criteria. In addition, influence and othermetrics and attribute patterns may be used to determine similaritiesbetween objects. Thus, the adaptive recommendations function 900 mayconnect objects which have not been in contact before, providing theuser a targeted recommendation, and generating a relationship betweenthose objects. That newly formed relationship may cascade to affectother objects in the system such as communities and topics

Finally, the adaptive recommendations function 900 may track usage ofadaptive recommendations (block 918). As the embedded algorithms areoptimized for speed and real-time performance for in-contextrecommendations, the ‘understanding’ and true relevance (as inferredfrom user usage behavior) of the adaptive recommendations may beprocessed later As such, tracking the selection and usage of adaptiverecommendations at this time may be beneficial Criteria such asplacement position on a list or other display mechanism, determined(estimated) relevance as predicted by the algorithm versus firstselections by the user, and choice of object type (such as article,subject matter expert, multimedia, image etc), are just a few examplesof how the adaptive recommendations function may self-monitor itsperformance. This performance analysis may ultimately generate betterquality recommendations for the user, and be used in updating systemstructure such as EHRA inputs. Or, the system may be self-policing, ineffect, making changes as usage data builds up.

It should be noted that the adaptive recommendations function 900depicted in FIG. 43 is a simplified embodiment of the adaptiverecommendations function 240, as one algorithm of possibly many isexamined Many complex variations of the recommendations algorithms maybe implemented, in accordance with the descriptions of the adaptivesystem 100 of FIG. 1 and the adaptive recombinant system 800 of FIG. 18,above.

Further Example Embodiment Description

The screenshot 770 also depicts a user personal library function 714,denoted “My Personal Topics,” for a particular user. A screenshot 720 inFIG. 45 illustrates the use of the adaptive recommendations function, asshown in a “Recommended For You” graphic 722, with a list ofsuggestions. A “My Path” graphic 724 also with a list, represents thepath of objects the user has already browsed. The recommendations in 722adapt as the user browses different objects.

In the screen image 790 of FIG. 40, the ‘MyPath’ function represents thejourney a user has made in the network during their session. The usermay browse the list of objects that they have visited during a session.There are further options to save an object as part of their My Worldpersonal library and also to remove an object from their path. TheMyPath function way be useful to users in identifying areas of thenetwork they have browsed before, and users may also elect to share aspecific path or all paths with other users of the system.

Path data can be used to strengthen adaptive recommendations on anautomatic basis, while also contributing to input of an automatic orsemi-automatic recommendation for the setup of a new community or newtopical area.

Cumulative usage data may also be of interest to users of the system asillustrated in the screen image 780 of FIG. 39 Table 782 shows anexample of usage patterns shown on a temporal bases to reflect amount ofinterest in certain topical areas. While human users of the system canbe easily overwhelmed with the amount of statistical informationgenerated by usage data of many different kinds, the screen imagedisplays the information in a manner so as to express multifaceted datafor input into its adaptive recommendation functions.

Automatic Fuzzy Content Network Maintenance

The adaptive recommendations function and related sets of algorithms, inconjunction with the fuzzy network maintenance functions, may be used toautomatically or semi-automatically update and enhance the fuzzy contentnetwork. These functions may be employed to determine new affinities andthe appropriate degree of relationship among fuzzy network objects inthe fuzzy network as a whole, within personal fuzzy network subsets, orsub-community-specific fuzzy network subsets. The automatic updating mayinclude potentially setting a relationship between any two objects tozero (effectively deleting a relationship link).

The recommendation function and fuzzy network maintenance functions mayoperate completely automatically, performing in the background andupdating affinities independently of human intervention, or the functionmay be used by users or special experts who rely on the adaptiverecommendations to provide guidance in maintaining the fuzzy network asa whole, or maintaining specific fuzzy network subsets.

In either an autonomous mode of operation, or in conjunction with humanexpertise, the recommendation function may be used to integrate newcontent or content objects into the fuzzy content network.

As in the case of adaptive recommendations that are delivered torecipients to enhance their ability to effectively navigate and use thesystem, adaptive recommendations that function to update the fuzzycontent network include algorithms that make inferences from the usagebehaviors of system users. These inferences may be at the communitylevel, sub-community level, or individual user level. Usage behaviorsthat may be included in the inferencing include online informationaccesses, traffic patterns and click streams associated with navigatingthe system structure, including buying and selling behaviors; physicallocational information associated with stationary or mobile use of thesystem; collaborative behaviors among system users or systems users andpeople outside the system, that include written and oral communications;referencing behaviors of system users—for example, the tagging ofinformation for future reference; subscription and other self-profilingbehavior of users; and direct feedback behaviors, such as the ratings ordirect written feedback associated with objects or their attributes suchas the objects' author, publisher, etc. The algorithms may also useinformation associated with temporal information associated with usagebehaviors, including the duration of behaviors and the timing of thebehaviors, where the behaviors may include those associated with readingor writing of written or graphical material, oral communications,including listening and talking, or duration of physical location of asystem user.

In some embodiments, inferences regarding a plurality of usage behaviorsmay be used to adjust relationships and associated relationship valuesand indicators, as explained in the sample embodiment above. These fuzzynetwork structural modifications may be applied to multiple relationshiptypes. Navigational access information may be used by the algorithms;that is, the relative level of traffic between two objects (each eithera content object or a topic object) will influence the degree ofrelationship between the two objects. However, access information aloneis likely to be insufficient for best results as navigation accesses arehighly influenced by the current system structure, and therefore currentstructures would tend to be reinforced, limiting the level ofadaptation. Therefore, other or additional behavioral information ispreferentially used to overcome this bias. For example, duration ofviewing objects typically provides a better indication of value of anobject to a user than does just an object access, as does, for example,reference and reference organization cues, collaboration cues, anddirect feedback. Therefore, this additional behavioral information maybe used to adjust the strengths of relationships among objects.

As an example, where referenced or tagged information can be organizedby users, the system may scan the referenced information and how it isorganized, and the frequency of the organizational structures amongusers, to determine a preliminary degree of relationships in the system.This may be augmented by information associated with navigationalaccesses and the duration of the accesses.

As a simplified example, FIG. 44A depicts a simple fuzzy network 670abefore application of the recommendation function and associated fuzzynetwork maintenance functions. FIG. 44B depicts fuzzy network 670b,resulting from the application of the recommendation function andassociated fuzzy network maintenance functions to fuzzy network 670a.(For the sake of simplicity, relationship indicators are not shown.)

The fuzzy network 670a may have a popular access path 672a from Node Xto Node Y which in turn has a popular access path 674a to Node Z.Assuming the existing relationships along that path are of similarstrength, it might suggest, without any additional information, thatthese relationships should perhaps be strengthened due to the highpopularity of the path. However, more usage behavioral information maysuggest a different fuzzy network updating approach. For example, theduration of accesses of Node X and Node Z were generally significantlyhigher than for Node Y, a better structural update might be to increase,or establish, the relationship between Node X and Node Z, as is shown inthe fuzzy network 670b. After application of an algorithm thatincorporates the durational usage behavioral cues, a relationship 676bis established between Node X and Node Z. In addition, in this example,the former relationship 672a between Node X and Node Y is deleted (inpractice, it might just be weakened in strength).

The structural transformation from fuzzy network 670a to 670b as shownwould be even more reinforced if additional usage behavioral informationsupported reinforced the access durational-based inferences onpreferences. For example, if Node X and Node Z were more frequentlyreferenced by users than Node Y, and were organized such as to implyclose affinity (for example, stored in the same personal topical area).This would be more confirming information to strengthen the relationshipbetween Node X and Node Z, and to weaken or eliminate the relationshipbetween Node X and Node Y.

The relationship updating algorithm may temper potential relationshipupdating, including adding new relationships, with global considerationsrelated to optimal connections among network objects. For example, toofew relationships, or relationships with insufficient spread of strengthvalues tend to inhibit effective navigation, but on the other hand toomany relationships also is not optimal. The algorithm may strive tomaintain an optimal richness of relationships while updating the fuzzycontent network based on usage characteristics. The algorithm may usepreferential distributions based on fuzzy network metrics such asconnectedness and influence to optimize the fuzzy network relationshiptopologies.

The recommendation function or related algorithms, in conjunction withthe fuzzy content network maintenance functions, may also be extended toscan, evaluate, and determine fuzzy network subsets that have specialcharacteristics. For example, the recommendation function or relatedalgorithms may suggest that certain of the fuzzy network subsets thathave been evaluated are candidates for special designation. This mayinclude being a candidate for becoming a topical area. Therecommendation function may suggest to human users or experts the fuzzynetwork subset that is suggested to become a topical area, along withexisting topical areas that are deemed by the recommendation function tobe “closest” in relationship to the new suggested topical area. A humanuser or expert may then be invited to add a topic, along with associatedmeta-information, and may manually create relationships between the newtopic and existing topics. Statistical pattern matching or learningalgorithms used to identify such fuzzy network subsets may include, butare not limited to, semantic network techniques, Bayesian analyticaltechniques, neural network-based techniques, k-nearest neighbor, supportvector machine-based techniques, or other statistical analyticaltechniques.

The algorithms may apply fuzzy network usage behaviors, along with usercommunity segmentations, to determine new topical areas. The algorithmsmay be augmented with global considerations related to optimaltopologies of fuzzy network structures so as to deliver the mosteffective usability. For example, too many topics, or topics notsufficiently spread across the over domain of information or knowledgeaddressed by the system, tend to inhibit effective navigation and use.The algorithm may strive to maintain an optimal richness of topicalareas. The algorithm may use preferential distributions based on fuzzynetwork metrics such as connectedness and influence to optimize thefuzzy network relationship topologies. This approach may also beemployed in suggesting topical areas for deletion.

Or, the recommendation function or related algorithms, in conjunctionwith the fuzzy content network maintenance functions, may automaticallygenerate the topic object and associated meta-information, and mayautomatically generate the relationships and relationship indicators andtheir values between the newly created topic object and other topicobjects in the fuzzy network.

In some embodiments this capability may be extended such that therecommendation function or related algorithms, along with fuzzy networkmaintenance functions, automatically maintain the fuzzy network andidentified fuzzy network subsets. The recommendation function may notonly identify new topical areas, generate associated topic objects,associated relationships and relationship indicators among the new topicobjects and existing topic objects, and the associated values of therelationships indicators, but also identify topic objects that arecandidates for deletion, and in some embodiments may automaticallydelete the topic object and its associated relationships.

The adaptive recommendations function, in conjunction with the fuzzynetwork maintenance functions, may likewise identify content objectsthat are candidates for deletion, and may automatically delete theassociated content objects and their associated relationships.

In this way the adaptive recommendations function or related algorithms,along with the fuzzy content network maintenance functions, mayautomatically adapt the structure of the fuzzy network itself on aperiodic or continuous basis to enable the best possible experience forthe fuzzy network's users.

As in network embodiments, when a new fuzzy content network isinitialized, the adaptive recommendation function may also serve as atraining mechanism for the new network. Given a distribution of content,relationships and relationships types, metrics and usage behaviorsassociated with scope, subject and other experiential data of otherfuzzy content networks, a module of the adaptive recommendation functionmay automatically begin assimilation of content objects into a fuzzycontent network, with intervention as required by humans. Clusters ofnewly assimilated content objects may enable inferences resulting in thesuggestion of new topical objects and communities, and associatedrelationship types and indicators may also be automatically created andupdated. This functionality of the adaptive recommendation engine mayalso be applied when two or more fuzzy content networks are broughttogether and require integration.

Each of the automatic steps listed above may be interactive with humanusers and experts as desired.

Social Network Analysis in Fuzzy Content Object Networks

Social network analysis may be conducted with adaptive recombinantsystem 800 in multiple ways. First, the representation of a person orpeople may be explicitly through content objects in the fuzzy contentnetwork. Special people-type content objects may be available, forexample. Such a content object may have relevant meta-information suchas an image of the person, and associated biography, affiliatedorganization, contact information, etc. The content object may berelated to other content objects that the person or persons personallycontributed to, topics that they have particular interest or expertisein, or any other system objects with which the person or persons have anaffinity. Tracking information associated with access to these contentobjects by specific users, and/or user sub-communities may be determinedas described above.

Furthermore, collaborative usage patterns may be used to understanddirect communications interactions among persons, in addition toindirect interactions (e.g., interactions related to the contentassociated with a person). The physical location of people may betracked, enabling an inference of in-person interactions, in addition tocollaborations at a distance.

Second, specific people may be associated with specific content andtopic objects—for example, the author of a particular content object.These people may or may not have explicit associated people-type contentobjects. Metrics related to the popularity, connectedness, and influenceof a person's associated content may be calculated to providemeasurement and insights associated with the underlying social network.The associations with content objects may be with a group of peoplerather than a single individual such as an author. For example, themetrics may be calculated for organizations affiliated with contentobjects. An example is the publisher of the associated content.

In either of the approaches described above, report-based andgraphical-based formats may be used to display attributes of theunderlying social network. These may include on-line or printed displaysthat illustrate how communities or sub-communities of users directlyaccess a set of people (through the associated content objects), orindirectly through associated content affiliated with the set of people.

Adaptive Processes and Process Networks

The adaptive system 100 and the adaptive recombinant system 800 enablethe effective implementation of computer-based or computer-assistedprocesses. Processes involve a sequence of activity steps or stages thatmay be explicitly defined, and such sequences are sometimes termed“workflow.” These processes may involve structures that require, orencourage, a step or stage to be completed before the next step or stagemay be conducted. Additional relevant details on process-basedapplications and implementations of adaptive networks is disclosed inU.S. Provisional Patent Application, No. 60/572,565, entitled “A Methodand System for Adaptive Processes,” which is incorporated herein byreference, as if set forth in its entirety.

A set of relationships and associated relationship indicators may beemployed to designate process flows among objects in a fuzzy network, orfuzzy content network. The existence of a process relationship betweenobject x and object y implies that x precedes y in a specified process.A process relationship may exist between object x and a plurality ofother objects. In these embodiments, a user may have a choice ofmultiple process step options from an originating process step. Thevalues of a plurality relationship indicators associated with theprocess relationships between an object and a plurality of objects maybe different.

A plurality of process relationship indicators may be designated amongthe objects in a fuzzy content network, which enables objects to beorganized in a plurality of processes.

Display functions enable a user to navigate through a fuzzy network orfuzzy network subset via objects that have process relations betweenthem. At each process step, corresponding to accessing the correspondingobject, the user may have the ability to navigate to other relatedobjects, which can be advantageous in providing the user with relevantinformation to facilitate executing the corresponding process step.

Fuzzy processes may be organized into fuzzy sub-processes throughselection of a subset of objects corresponding to a contiguous set ofprocess steps, along with all other objects related to the process stepobjects, or more generally, as the set of all objects within a specifiedfractional degrees of separation from each of the process step objects.

New fuzzy processes may be generated by combining fuzzy processsub-networks into new fuzzy process networks using the fuzzy networkunion, intersection and other operators.

FIG. 45 depicts various hardware topologies that the adaptive system 100or the adaptive recombinant system 800 may embody. Servers 950, 952, and954 are shown, perhaps residing a different physical locations, andpotentially belonging to different organizations or individuals. Astandard PC workstation 956 is connected to the server in a contemporaryfashion. In this instance, the systems 100 or 800 may reside on theserver 950, but may be accessed by the workstation 956. A terminal ordisplay-only device 958 and a workstation setup 960 are also shown. ThePC workstation 956 may be connected to a portable processing device (notshown), such as a mobile telephony device, which may be a mobile phoneor a personal digital assistant (PDA). The mobile telephony device orPDA may, in turn, be connected to another wireless device such as atelephone or a GPS receiver.

FIG. 45 also features a network of wireless or other portable devices962. The adaptive system 100 or the adaptive recombinant system 800 mayreside, in part or as a whole, on all of the devices 962, periodicallyor continuously communicating with the central server 952, as required.A workstation 964 connected in a peer-to-peer fashion with a pluralityof other computers is also shown. In this computing topology, thesystems 100 or 800, as a whole or in part, may reside on each of thepeer computers 964.

Computing system 966 represents a PC or other computing system whichconnects through a gateway or other host in order to access the server952 on which the systems 100 or 800 reside. An appliance 968, includessoftware “hardwired” into a physical device, or may utilize softwarerunning on another system that does not itself host the systems 100 or800. The appliance 968 is able to access a computing system that hostsan instance of the system 100 or 800, such as the server 952, and isable to interact with the instance of the system 100 or 800. P While thepresent invention has been described with respect to a limited number ofembodiments, those skilled in the art will appreciate numerousmodifications and variations therefrom. It is intended that the appendedclaims cover all such modifications and variations as fall within thetrue spirit and scope of this present invention.

What is claimed is:
 1. An adaptive recommendation system, comprising: atleast one storage device configured to store a plurality of aspectscomprising: a content aspect comprising information; acomputer-implemented structural aspect comprising the content aspect andassociated relationships; and a usage aspect, comprising captured usagebehaviors, wherein the usage behaviors are associated with one or moreusers of the system; and at least one processing device configured toexecute a plurality of functions comprising: a function to generate auser tunable adaptive recommendation based, at least in part, on auser's navigational context and on an automatic inference of the user'sinterests from a plurality of usage behaviors associated with the one ormore users corresponding to a plurality of usage behavior categories;and a function to deliver the adaptive recommendation to the user one ormore users.
 2. The adaptive recommendation system of claim 1, whereinthe information is selected from a group consisting of text, graphics,audio, video, interactive forms of content, applets, tutorials,advertising content, courseware, demonstrations, representations ofpeople, modules, executable code, and computer programs.
 3. The adaptiverecommendation system of claim 1, wherein the computer-implementedstructural aspect further comprises: one or more objects, each objectcomprising the information; and one or more relationships, wherein eachrelationship is associated with each pair of the one or more objects. 4.The adaptive recommendation system of claim 1, the usage aspect furthercomprising one or more usage behaviors, wherein each usage behavior isassociated with either a user, one or more user communities, or a theuser and the one or more user communities simultaneously, wherein theuser comprises a single-member subset of the one or more users and acommunity of the one or more user communities comprises amultiple-member subset of the one or more users.
 5. The adaptiverecommendation system of claim 1, wherein a user of the one or moreusers is selected from a group consisting of a computer-based system, asecond adaptive system, and a human being.
 6. The adaptiverecommendation system of claim 1, the plurality of usage behaviorsfurther comprising private behaviors and non-private behaviors.
 7. Theadaptive recommendation system of claim 1, further comprising a privacycontrol, the privacy control enabling a user of the one or more users torestrict usage behaviors associated with the user from being deemednon-private behaviors.
 8. The adaptive recommendation system of claim 1,wherein a the function to generate a the user tunable adaptiverecommendation based, at least in part, on a user's the navigationalcontext of the one or more users and on an the automatic inference ofthe user's interests of the one or more users from a the plurality ofusage behaviors associated with the one or more users corresponding to athe plurality of usage behavior categories further comprises: usagebehavior categories, wherein the usage behavior categories are selectedfrom a group consisting of navigation and access patterns, collaborativepatterns, direct feedback patterns, subscription patterns,self-profiling patterns, reference patterns, and physical locationpatterns.
 9. The adaptive recommendation system of claim 1, wherein athe function to generate a the user tunable adaptive recommendationbased, at least in part, on a user's the navigational context of the oneor more users and on an the automatic inference of the user's interestsof the one or more users from a the plurality of usage behaviorsassociated with the one or more users corresponding to a the pluralityof usage behavior categories further comprises: a function that infersuser preferences from the plurality of usage behaviors.
 10. The adaptiverecommendation system of claim 9, wherein a the function that infersuser preferences from the plurality of usage behaviors furthercomprises: an algorithm that prioritizes application of usage patternsassociated with a the plurality of usage behavior categories.
 11. Theadaptive recommendation system of claim 9, wherein a the function thatinfers user preferences the algorithm from the plurality of usagebehaviors comprises a statistical learning algorithm, wherein thestatistical learning algorithm is selected from a group consisting ofBayesian modeling, neural network modeling, k-nearest neighbor modeling,and support vector machine modeling.
 12. The adaptive recommendationsystem of claim 1, wherein a the function to generate a the user tunableadaptive recommendation based, at least in part, on a user's thenavigational context of the one or more users and on an the automaticinference of the user's interests of the one or more users from a theplurality of usage behaviors associated with the one or more userscorresponding to a the plurality of usage behavior categories furthercomprises: an algorithm that detects apparent insincere system usagebehaviors or other inferred “gaming” gaming behaviors by the one or moreusers.
 13. The adaptive recommendation system of claim 1, wherein a thefunction to generate a the user tunable adaptive recommendation based,at least in part, on a user's the navigational context of the one ormore users and on an the automatic inference of the user's interests ofthe one or more users from a the plurality of usage behaviors associatedwith the one or more users corresponding to a the plurality of usagebehavior categories further comprises: a compensatory algorithmassociated with the detection of apparent insincere system usagebehaviors or other inferred “gaming” gaming behaviors by the one or moreusers.
 14. The adaptive recommendation system of claim 1, wherein a thefunction to generate a the user tunable adaptive recommendation based,at least in part, on a user's the navigational context of the one ormore users and on an the automatic inference of the user's interests ofthe one or more users from a the plurality of usage behaviors associatedwith the one or more users corresponding to a the plurality of usagebehavior categories further comprises: an algorithm that applies patternmatching of information embodied in the structural aspect and contentaspect to produce content interpretation patterns, and associates thecontent interpretation patterns with usage patterns.
 15. The adaptiverecommendation system of claim 1, wherein the user tunable adaptiverecommendation further comprises: a structural subset of the structuralaspect, the structural subset comprising at least one of the one or moreobjects and associated relationships of the one or more objects of thestructural aspect.
 16. The adaptive recommendation system of claim 1,wherein a the function to deliver the adaptive recommendation to theuser one or more users comprises: a recommendation delivery mode,wherein the recommendation delivery mode is selected from a groupconsisting of visual, audio, and a combination of visual and audio. 17.The adaptive recommendation system of claim 1, wherein a the function todeliver the adaptive recommendation to the user one or more userscomprises: a recommendation delivery means, wherein the recommendationdelivery means is selected from a group consisting of user in-contextsystem usage, direct user requests, and out-of-the-context of systemusage.
 18. A mobile adaptive recommendation system, comprising: at leastone storage device configured to store a plurality of aspectscomprising: a content aspect comprising information; acomputer-implemented structural aspect comprising the content aspect andassociated relationships; and a usage aspect, comprising captured usagebehaviors, wherein the usage behaviors are associated with one or moreusers; and at least one processing device configured to execute aplurality of functions comprising: a function to automatically determinethe location of a user based on physical location data generated by alocation-aware device; a user-controlled recommendation tuning function;a function to generate an adaptive recommendation based, at least inpart, on the user's recommendation tuning settings and on theautomatically determined location of the user and at least one other ofthe usage behavior behaviors associated with the one or more userscorresponding to at least one other usage behavior category; and afunction to deliver the adaptive recommendation to the user.
 19. Themobile adaptive recommendation system of claim 18, wherein a thefunction to generate an the adaptive recommendation based, at least inpart, on the user's recommendation tuning settings and on theautomatically determined location of the user and at least one otherusage behavior associated with the one or more users corresponding to atleast one other usage behavior category further comprises: an algorithmto determine the change in location of a the user as a function of time.20. An article comprising a physical non-transitory computer-readablemedium storing instructions for enabling a processor-based system to:access a content aspect comprising information; access a structuralaspect comprising the content aspect and associated relationships;access a usage aspect, comprising captured usage behaviors, wherein theusage behaviors are associated with one or more users; generate an usertunable adaptive recommendation based, at least in part, on a user'snavigational context and on an automatic inference of the user'sinterests from a plurality of usage behaviors associated with the one ormore users corresponding to a plurality of usage behavior categories;and deliver the adaptive recommendation to the user.
 21. An adaptiverecommendation system, comprising: at least one storage deviceconfigured to store a plurality of aspects comprising: a content aspectcomprising information; a structural aspect comprising the contentaspect and associated relationships; and a usage aspect comprisingcaptured usage behaviors associated with users of the adaptiverecommendation system and corresponding to a plurality of usage behaviorcategories; and at least one processing device configured to execute: afunction to generate user tunable adaptive recommendations based, atleast in part, on a navigational context of the users and on anautomatic inference of interests of the users from a plurality of thecaptured usage behaviors associated with the users and corresponding tothe plurality of usage behavior categories; and a function to deliverthe user tunable adaptive recommendations to at least one of the users.22. The adaptive recommendation system of claim 21, wherein theinformation includes text, graphics, audio, video, interactive forms ofcontent, applets, tutorials, advertising content, courseware,demonstrations, representations of people, modules, executable code, orcomputer programs.
 23. The adaptive recommendation system of claim 21,wherein the structural aspect further includes objects with at least aportion of the information; and wherein each of the associatedrelationships is configured to associate a pair of the objects.
 24. Theadaptive recommendation system of claim 21, wherein the captured usagebehaviors are associated with either the users or one or more usercommunities or the users and the one or more user communities; whereineach of the users comprises a single-member subset of the users; andwherein a community of the one or more user communities comprises amultiple-member subset of the users.
 25. The adaptive recommendationsystem of claim 21, wherein the users are selected from a groupconsisting of a computer-based system, a second adaptive system, and ahuman being.
 26. The adaptive recommendation system of claim 21, whereinthe captured usage behaviors further comprise private behaviors andnon-private behaviors.
 27. The adaptive recommendation system of claim21, further comprising a privacy control configured to enable the usersto restrict the captured usage behaviors from being deemed non-privatebehaviors.
 28. The adaptive recommendation system of claim 21, whereinthe plurality of usage behavior categories includes navigation andaccess patterns, collaborative patterns, direct feedback patterns,subscription patterns, self-profiling patterns, reference patterns, orphysical location patterns.
 29. The adaptive recommendation system ofclaim 21, wherein the function to generate the user tunable adaptiverecommendations includes a function configured to infer preferences ofthe users from the captured usage behaviors.
 30. The adaptiverecommendation system of claim 29, wherein the function configured toinfer preferences of the users from the captured usage behaviorsincludes an algorithm configured to prioritize application of usagepatterns associated with the plurality of usage behavior categories. 31.The adaptive recommendation system of claim 29, wherein the functionconfigured to infer preferences of the users from the captured usagebehaviors includes a statistical learning algorithm selected from agroup consisting of Bayesian modeling, neural network modeling,k-nearest neighbor modeling, and support vector machine modeling. 32.The adaptive recommendation system of claim 21, wherein the function togenerate user tunable adaptive recommendations includes an algorithmconfigured to detect apparent insincere system usage behaviors or otherinferred gaming behaviors by the users.
 33. The adaptive recommendationsystem of claim 21, wherein the function to generate user tunableadaptive recommendations includes a compensatory algorithm configured todetect apparent insincere system usage behaviors or other inferredgaming behaviors by the users.
 34. The adaptive recommendation system ofclaim 21, wherein the function to generate user tunable adaptiverecommendations includes an algorithm configured to apply patternmatching of the information embodied in the structural aspect and thecontent aspect to produce content interpretation patterns and configuredto associate the content interpretation patterns with usage patterns.35. The adaptive recommendation system of claim 21, wherein a structuralsubset of the structural aspect includes at least one object and whereinthe relationships are associated with the least one object.
 36. Theadaptive recommendation system of claim 21, wherein the function todeliver the user tunable adaptive recommendations includes arecommendation delivery mode; and wherein the recommendation deliverymode is selected from a group consisting of visual, audio, and acombination of visual and audio.
 37. The adaptive recommendation systemof claim 21, wherein the function to deliver the user tunable adaptiverecommendations includes a delivery means comprising in-context systemusage by the users, direct requests by the users, or out-of-contextsystem usage.
 38. A mobile adaptive recommendation system, comprising:at least one storage device configured to store a plurality of aspectscomprising: a content aspect comprising information; a structural aspectcomprising the content aspect and associated relationships; and a usageaspect comprising captured usage behaviors associated with a user; andat least one processing device configured to execute: a function toautomatically determine a location of the user based on physicallocation data generated by a location-aware device; a user-controlledrecommendation tuning function; a function to generate an adaptiverecommendation based, at least in part, on recommendation tuningsettings, the determined location of the user, and at least one of thecaptured usage behaviors associated with the user and corresponding toat least one usage behavior category; and a function to deliver theadaptive recommendation to the user.
 39. The mobile adaptiverecommendation system of claim 38, wherein the function to generate theadaptive recommendation includes an algorithm to determine a change inthe location of the user as a function of time.
 40. An articlecomprising a non-transitory computer-readable medium storinginstructions that, in response to execution by a processing device,cause the processing device to perform operations comprising: access acontent aspect comprising information; access a structural aspectcomprising the content aspect and associated relationships; access ausage aspect comprising captured usage behaviors associated with a user;generate a user tunable adaptive recommendation based, at least in part,on a navigational context of the user and on an automatic inference ofinterests of the user from the captured usage behaviors associated withthe user and corresponding to usage behavior categories; and deliver theuser tunable adaptive recommendation to the user.